{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Week 3\n",
    "\n",
    "**`Agent::pre-training::quadratic-programming`**\n",
    "* **`optimizer`**: general objective function\n",
    "* **`generator`**: supervised data generator\n",
    "* **`QuadraticAgent`**: trading agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/alpha/Developer/qtrader\n"
     ]
    }
   ],
   "source": [
    "# change current working directory\n",
    "%cd .."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# suppress warning messages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# built-in containers\n",
    "from collections import deque\n",
    "\n",
    "# data provider\n",
    "from qtrader.envs.data_loader import Finance\n",
    "# pandas.DataFrame cleaner\n",
    "from qtrader.utils.pandas import clean\n",
    "# machine floating-point relative accuracy\n",
    "from qtrader.utils.numpy import eps\n",
    "# generate rolling data for a 2D array\n",
    "from qtrader.utils.preprocessor import rolling2d\n",
    "# trading environment\n",
    "from qtrader.envs.trading import TradingEnv\n",
    "# random trading agent\n",
    "from qtrader.agents import RandomAgent\n",
    "\n",
    "# YAML parser\n",
    "import yaml\n",
    "\n",
    "# scientific programming\n",
    "import numpy as np\n",
    "import scipy.optimize\n",
    "\n",
    "# visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start date: 2015-01-01\n",
      "trading frequency: W-FRI\n",
      "trading universe: ['AAPL', 'GE', 'JPM', 'MSFT', 'VOD', 'GS', 'TSLA', 'MMM']\n",
      "rolling window size: 20\n",
      "transaction costs coefficient: 0.0025\n"
     ]
    }
   ],
   "source": [
    "# fetch configuration file\n",
    "config = yaml.load(open('config/log/week_3.yaml', 'r'))\n",
    "\n",
    "# configuration summary\n",
    "print(f\"start date: {config['start_date']}\")\n",
    "print(f\"trading frequency: {config['freq']}\")\n",
    "print(f\"trading universe: {config['tickers']}\")\n",
    "print(f\"rolling window size: {config['window']}\")\n",
    "print(f\"transaction costs coefficient: {config['beta']}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Data Source\n",
    "\n",
    "Fetch prices, simple-relative returns and log-returns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# prices\n",
    "prices = clean(Finance.Prices(config['tickers'],\n",
    "                              config['start_date'],\n",
    "                              freq=config['freq'],\n",
    "                              csv=config['csv_file_prices']))\n",
    "\n",
    "\n",
    "# returns\n",
    "returns = clean(Finance.Returns(config['tickers'],\n",
    "                                config['start_date'],\n",
    "                                freq=config['freq'],\n",
    "                                csv=config['csv_file_returns']))\n",
    "\n",
    "# log-returns\n",
    "rhos = np.log(1 + returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Empirical Estimators\n",
    "\n",
    "First and second order moments empirical estimators and visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# mean returns\n",
    "mu_r = returns.mean()\n",
    "# returns covariance\n",
    "Sigma_r = returns.cov()\n",
    "\n",
    "fig, axes = plt.subplots(ncols=2, figsize=(18.0, 6.0))\n",
    "\n",
    "mu_r.plot.bar(ax=axes[0])\n",
    "axes[0].set_title('Mean Returns')\n",
    "\n",
    "sns.heatmap(Sigma_r, ax=axes[1])\n",
    "axes[1].set_title('Covariance Matrix');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## `optimizer`\n",
    "\n",
    "General quadratic programming solver, accepting any objective\n",
    "function $J$ to solve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def optimizer(J, *args):\n",
    "    \"\"\"High-Order-Function receiving an objective\n",
    "    function `J` and returning an quadratic\n",
    "    programming solver.\"\"\"\n",
    "    def _optimizer(mu: np.ndarray, Sigma: np.ndarray,\n",
    "                   w0: np.ndarray, short_sales: bool = True) -> np.ndarray:\n",
    "        # number of assets\n",
    "        M = mu.shape[0]\n",
    "        # equality constraint: budget\n",
    "        con_budget = {\n",
    "            'type': 'eq',\n",
    "            'fun': lambda w: np.sum(w) - 1.0\n",
    "        }\n",
    "        if short_sales:\n",
    "            bounds = [(0, None) for _ in range(M)]\n",
    "        else:\n",
    "            bounds = [(None, None) for _ in range(M)]\n",
    "        # execute\n",
    "        results = scipy.optimize.minimize(\n",
    "            J, w0, (mu, Sigma, w0, *args),\n",
    "            constraints=(con_budget),\n",
    "            bounds=bounds,\n",
    "            method='SLSQP'\n",
    "        )\n",
    "        # handle errors\n",
    "        if not results.success:\n",
    "            raise BaseException(results.message)\n",
    "        # optimal portfolio weights\n",
    "        w = results.x\n",
    "        return w\n",
    "    return _optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "source": [
    "### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def _mu_p(w: np.ndarray, r: np.ndarray) -> float:\n",
    "    \"\"\"Portfolio Returns.\"\"\"\n",
    "    return np.dot(w.T, r)\n",
    "\n",
    "\n",
    "def _sigma_p(w: np.ndarray, Sigma: np.ndarray) -> float:\n",
    "    \"\"\"Portoflio Variance\"\"\"\n",
    "    return np.dot(np.dot(w.T, Sigma), w)\n",
    "\n",
    "\n",
    "def _trans_costs(w: np.ndarray, w0: np.ndarray, coef: float) -> float:\n",
    "    \"\"\"Transaction Costs.\"\"\"\n",
    "    return np.sum(np.abs(w0 - w)) * coef"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### $J_{1}$: Risk Averse Portfolio with Transaction Costs\n",
    "\n",
    "Given $\\alpha, \\beta$, determine portfolio vector $\\mathbf{w}$, such that:\n",
    "\n",
    "\\begin{aligned}\n",
    "& \\underset{\\mathbf{w}}{\\text{maximize}}\n",
    "& & \\mathbf{w}^{T} \\mathbf{r} -\n",
    "    \\alpha \\mathbf{w}^{T} \\boldsymbol{\\Sigma} \\mathbf{w} -\n",
    "    \\mathbf{1}^{T} \\beta \\|(\\mathbf{w_{0}} - \\mathbf{w})\\|_{1} & \\text{ (utility function) } \\\\\n",
    "& \\text{subject to}\n",
    "& & \\mathbf{w}^{T} \\mathbf{1} = 1 & \\text{ (budget constraint) } \\\\\n",
    "&\n",
    "& & \n",
    "\\end{aligned}\n",
    "\n",
    "where:\n",
    "\n",
    "* $M$: number of assets in portfolio\n",
    "* $\\alpha \\geq 0$: risk-aversion coefficient\n",
    "* $\\beta \\geq 0$: transaction costs coefficient (i.e 0.0025)\n",
    "* $\\boldsymbol{\\Sigma} \\in \\mathbb{R}^{M \\times M}$: portfolio returns covariance\n",
    "* $\\mathbf{r} \\in \\mathbb{R}^{M}$: portfolio returns mean\n",
    "* $\\mathbf{w_{0}}$: initial portfolio weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def risk_aversion(w: np.ndarray, mu: np.ndarray,\n",
    "                  Sigma: np.ndarray, w0: np.ndarray,\n",
    "                  alpha: float, beta: float) -> float:\n",
    "    \"\"\"Risk Aversion with Transaction Costs.\"\"\"\n",
    "    assert Sigma.shape[0] == Sigma.shape[1]\n",
    "    assert mu.shape[0] == Sigma.shape[0]\n",
    "    assert w.shape == w0.shape\n",
    "    # mean - alpha * variance - transaction_costs\n",
    "    return - (_mu_p(w, mu) - alpha * _sigma_p(w, Sigma) - _trans_costs(w, w0, beta))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# objective function\n",
    "J_1 = risk_aversion\n",
    "\n",
    "# uniform initial weights\n",
    "w0 = np.ones_like(mu_r) / len(mu_r)\n",
    "\n",
    "# range of alpha\n",
    "alphas = np.linspace(0.001, 1000, 1000)\n",
    "\n",
    "# caches\n",
    "mus = np.empty_like(alphas)\n",
    "sigmas = np.empty_like(alphas)\n",
    "trans = np.empty_like(alphas)\n",
    "\n",
    "# run experiments\n",
    "for j, alpha in enumerate(alphas):\n",
    "    # optimal weights\n",
    "    wj = optimizer(J_1, alpha, config['beta'])(mu_r, Sigma_r, w0)\n",
    "    # portfolio expected mean returns\n",
    "    mus[j] = _mu_p(w=wj, r=mu_r)\n",
    "    # portfolio expected variance\n",
    "    sigmas[j] = _sigma_p(w=wj, Sigma=Sigma_r)\n",
    "    # transaction costs\n",
    "    trans[j] = _trans_costs(w=wj, coef=config['beta'], w0=w0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, sharey=True, figsize=(20.0, 6.0))\n",
    "sc0 = axes[0].scatter(np.sqrt(sigmas), mus, c=trans, cmap=plt.cm.Reds)\n",
    "\n",
    "axes[0].set_title('Optimal Portfolios and Transaction Costs')\n",
    "axes[0].set_xlabel('Portfolio Standard Deviation, $\\\\sigma_{p}$')\n",
    "axes[0].set_ylabel('Portfolio Expected Returns, $\\\\bar{r}_{p}$')\n",
    "axes[0].set_ylim([mus.min() * 0.9, mus.max() * 1.1])\n",
    "axes[0].set_xlim([np.sqrt(sigmas).min() * 0.9, np.sqrt(sigmas).max() * 1.1])\n",
    "fig.colorbar(sc0, ax=axes[0])\n",
    "\n",
    "sc1 = axes[1].scatter(np.sqrt(sigmas), mus, c=alphas, cmap=plt.cm.Blues)\n",
    "axes[1].set_title('Optimal Portfolios for varying $\\\\alpha$')\n",
    "axes[1].set_xlabel('Portfolio Standard Deviation, $\\\\sigma_{p}$')\n",
    "axes[1].set_ylim([mus.min() * 0.9, mus.max() * 1.1])\n",
    "axes[1].set_xlim([np.sqrt(sigmas).min() * 0.9, np.sqrt(sigmas).max() * 1.1])\n",
    "fig.colorbar(sc1, ax=axes[1]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## $J_{2}$: Tangency Portfolio with Transaction Costs\n",
    "\n",
    "Given $\\beta$, determine portfolio vector $\\mathbf{w}$, such that:\n",
    "\n",
    "\\begin{aligned}\n",
    "& \\underset{\\mathbf{w}}{\\text{maximize}}\n",
    "& & \\frac{\\mathbf{w}^{T} \\mathbf{r} - \\mathbf{1}^{T} \\beta \\|(\\mathbf{w_{0}} - \\mathbf{w})\\|_{1}}\n",
    "        {\\sqrt{\\mathbf{w}^{T} \\boldsymbol{\\Sigma} \\mathbf{w}}}\n",
    "     & \\text{ (utility function) } \\\\\n",
    "& \\text{subject to}\n",
    "& & \\mathbf{w}^{T} \\mathbf{1} = 1 & \\text{ (budget constraint) } \\\\\n",
    "&\n",
    "& & \n",
    "\\end{aligned}\n",
    "\n",
    "where:\n",
    "\n",
    "* $M$: number of assets in portfolio\n",
    "* $\\beta \\geq 0$: transaction costs coefficient (i.e 0.0025)\n",
    "* $\\boldsymbol{\\Sigma} \\in \\mathbb{R}^{M \\times M}$: portfolio returns covariance\n",
    "* $\\mathbf{r} \\in \\mathbb{R}^{M}$: portfolio returns mean\n",
    "* $\\mathbf{w_{0}}$: initial portfolio weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def sharpe_ratio(w: np.ndarray, mu: np.ndarray,\n",
    "                 Sigma: np.ndarray, w0: np.ndarray,\n",
    "                 beta: float) -> float:\n",
    "    \"\"\"Sharpe Ratio with Transaction Costs.\"\"\"\n",
    "    assert Sigma.shape[0] == Sigma.shape[1]\n",
    "    assert mu.shape[0] == Sigma.shape[0]\n",
    "    assert w.shape == w0.shape\n",
    "    # mean - alpha * variance - transaction_costs\n",
    "    return - ((_mu_p(w, mu) - _trans_costs(w, w0, beta)) / (_sigma_p(w, Sigma) + eps))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected mean returns: 0.000612153870384461\n",
      "expected standard deviation: 9.708807167568885e-05\n",
      "transaction costs: 3.33333360913457e-13\n"
     ]
    }
   ],
   "source": [
    "# objective function\n",
    "J_2 = sharpe_ratio\n",
    "\n",
    "# uniform initial weights\n",
    "w0 = np.ones_like(mu_r) / len(mu_r)\n",
    "\n",
    "# range of alpha\n",
    "alphas = np.linspace(0.001, 1000, 1000)\n",
    "\n",
    "w = optimizer(J_2, config['beta'])(mu_r, Sigma_r, w0)\n",
    "# portfolio expected mean returns\n",
    "mu = _mu_p(w=w, r=mu_r)\n",
    "# portfolio expected standard deviation\n",
    "sigma = _sigma_p(w=w, Sigma=Sigma_r)\n",
    "# transaction costs\n",
    "trans = _trans_costs(w=w, coef=config['beta'], w0=w0)\n",
    "\n",
    "print(f\"expected mean returns: {mu}\")\n",
    "print(f\"expected standard deviation: {sigma}\")\n",
    "print(f\"transaction costs: {trans}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## `generator`\n",
    "\n",
    "Provided an objective function $J$ and historic data, generate pairs\n",
    "$\\{\\mathbf{x}_{i}, \\mathbf{y}_{i}\\}_{i=1}^{N}$ for pre-training\n",
    "in a supervised learning manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "def generator(num_samples, data, optimizer, window, short_sales=True):\n",
    "    \"\"\"Supervised data generator, following strategy\n",
    "    determined by `optimizer`.\"\"\"\n",
    "    # input data shape\n",
    "    N, M = data.shape\n",
    "    # generated dataset\n",
    "    X = np.empty((num_samples-window+1, window, M), dtype=float)\n",
    "    y = np.zeros((num_samples-window+1, M), dtype=float)\n",
    "    # iterate over rolling windows\n",
    "    for i, frame in enumerate(rolling2d(data, window)):\n",
    "        try:\n",
    "            # empirical mean estimate\n",
    "            mu_r = np.mean(frame, axis=0)\n",
    "            # empirical covariance estimate\n",
    "            Sigma_r = np.cov(data.T)\n",
    "            # initial random weights\n",
    "            w0 = np.random.uniform(0, 1.0, M)\n",
    "            w0 = w0 / np.sum(w0)\n",
    "            # observation\n",
    "            X[i, :, :] = frame\n",
    "            # optimal portfolio vector\n",
    "            y[i, :] = optimizer(mu_r, Sigma_r, w0, short_sales)\n",
    "        except BaseException as e:\n",
    "            print(\"[i=%d]\" % i, e)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "feature matrix shape: (712, 20, 6)\n",
      "target matrix shape: (712, 6)\n"
     ]
    }
   ],
   "source": [
    "# use tangency portfolio with transaction costs as objective function\n",
    "optim = optimizer(J_2, config['beta'])\n",
    "# create dataset\n",
    "X, y = generator(len(returns), returns, optim, config['window'])\n",
    "# verify validity of portfolio vectors\n",
    "np.testing.assert_array_almost_equal(y.sum(axis=1), np.ones(y.shape[0]))\n",
    "# dimensions\n",
    "print(f\"feature matrix shape: {X.shape}\")\n",
    "print(f\"target matrix shape: {y.shape}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## `QuadraticAgent`\n",
    "\n",
    "Quadratic programming trading agent, provided an objective function $J$.\n",
    "Folling API:\n",
    "- `observe(observation, action, reward, done, next_observation)` -> `None`\n",
    "- `act(observation)` -> `action: numpy.array`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "class QuadraticAgent:\n",
    "    \"\"\"Quadratic Programming agent.\"\"\"\n",
    "\n",
    "    _id = 'quadratic'\n",
    "\n",
    "    def __init__(self, action_space, J, window=10, *args):\n",
    "        self.optimizer = optimizer(self._J(J), *args)\n",
    "        self.action_space = action_space\n",
    "        self.memory = deque(maxlen=window)\n",
    "        # random initialization of weights\n",
    "        self.w = self.action_space.sample()\n",
    "\n",
    "    def observe(self, observation, action, reward, done, next_observation):\n",
    "        self.memory.append(observation.values)\n",
    "\n",
    "    def act(self, observation):\n",
    "        # deque -> np.array, for easy math\n",
    "        memory = np.array(self.memory)\n",
    "        # number of assets\n",
    "        M = len(observation)\n",
    "        # expected returns vector\n",
    "        mu = np.mean(memory, axis=0).reshape(M, 1)\n",
    "        if len(self.memory) != self.memory.maxlen:\n",
    "            sigma = np.eye(M)\n",
    "        else:\n",
    "            # empirical covariance matrix\n",
    "            sigma = np.cov(memory.T)\n",
    "        # quadratic programming portfolio solver\n",
    "        try:\n",
    "            self.w = self.optimizer(mu, sigma, self.w)\n",
    "        except BaseException:\n",
    "            pass\n",
    "        return self.w\n",
    "\n",
    "    @property\n",
    "    def name(self):\n",
    "        return self._id\n",
    "\n",
    "    def _J(self, J):\n",
    "        if J is \"sharpe_ratio\":\n",
    "            return sharpe_ratio\n",
    "        elif J is \"risk_aversion\":\n",
    "            return risk_aversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "# reproduce results\n",
    "np.random.seed(1)\n",
    "# initialize trading environment\n",
    "env = TradingEnv(prices=prices, trading_period=config['freq'])\n",
    "# quadratic agent\n",
    "tangent_agent = QuadraticAgent(env.action_space,\n",
    "                               'sharpe_ratio',\n",
    "                               config['window'],\n",
    "                               config['beta'])\n",
    "# random agent\n",
    "random_agent = RandomAgent(env.action_space)\n",
    "# register agents to environment\n",
    "env.register(tangent_agent)\n",
    "env.register(random_agent)\n",
    "# reset environment\n",
    "ob = env.reset()\n",
    "ob = ob['returns']\n",
    "reward = 0\n",
    "done = False\n",
    "while not done:\n",
    "    # fill agent's memory\n",
    "    tangent_agent.observe(ob, None, reward, done, None)\n",
    "    # infer best actions\n",
    "    tangent_action = tangent_agent.act(ob)\n",
    "    random_action = random_agent.act(ob)\n",
    "    # take actions\n",
    "    ob, reward, done, _ = env.step({tangent_agent.name: tangent_action,\n",
    "                                    random_agent.name: random_action})\n",
    "    # discard other fields than 'returns'\n",
    "    ob = ob['returns']\n",
    "# returns\n",
    "tangent_returns = env.agents[tangent_agent.name].rewards.sum(axis=1)\n",
    "random_returns = env.agents[random_agent.name].rewards.sum(axis=1)\n",
    "# compare PnLs\n",
    "tangent_pnl = (tangent_returns + 1).cumprod().values[-1]\n",
    "random_pnl = (random_returns + 1).cumprod().values[-1]\n",
    "# compare Sharpe Ratios\n",
    "tangent_sr = tangent_returns.mean() / (tangent_returns.std() + eps)\n",
    "random_sr = random_returns.mean() / (random_returns.std() + eps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tangent portfolio agent PnL: 1.3980475745087941\n",
      "random agent PnL: 1.4305051614801945\n",
      "tangent Sharpe Ratio: 0.16354693076507587\n",
      "random Sharpe Ratio: 0.13121001319386433\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvwAAAE2CAYAAAAdw0bAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvhp/UCwAAIABJREFUeJzs3Xd8VFXawPHfmZlk0pNJB0IKIZTQooAFEUFBXREV3UXF9RUUWUVlUWyoy2JHEUQFX1RElFVxUXwtiIKAYkEQEAJSQzrppJfJtPP+cSchgRACJEwmnO/nkw8ztz4z68597rnPOUdIKVEURVEURVEUpWPSuToARVEURVEURVHajkr4FUVRFEVRFKUDUwm/oiiKoiiKonRgKuFXFEVRFEVRlA5MJfyKoiiKoiiK0oGphF9RFEVRFEVROjCV8CvtghBirBBihxBCCiHGN7HeXwhRJoTIEEI8fZrnmCyESBdCLD3jgI8/dj8hxG/O+DcLIX4SQvwphHheCNHs/8+EEMlCiO6tHZOiKIpy+oQQoxpcl34UQvzs/F2f2oJ9H3Feb8qEEB+cjXgVpTlCjcOvtBdCiOHAN0CylPKiY9bdD7wMzJNSPnUG55gFxEopJ5xGbEullLHNbBMLpAEJUsoUIUQEsBd4TEr5TjP7BUkpS08lHkVRFKXtOX/7NwAeUkqbEKIP8AcwWkq59iT7zgJGSimHtnmginISqoVfaW+WA4OEEIPrFgghBDAK+N1lUZ0GKWU+2oXi6pNsp5J9RVEUNyCl/BPYxUl+1xWlvVEJv9LeZAJfAP9ssOxKYC3Q6HGUswxonRDieyHERiHEJc7l0Q3KayYIIdYIIWqdLfAN939SCFHlfFQbJ4TwEELMEUL86nx0+2+h6QnMByKFED8IIVacwufxAKxCiOuEEPuc55rjjC9NCDFXCFEqhJjQIK7bnOvXO/+ucC5vMj7nuiHOZeudMV57CjEqiqIoLecBTHSW7CwXQrwlhNguhPhGCOHl6uAUpSkq4Vfao9eBvwkhIp3v/wdY2sR2vsDfpJQjndt8DCClzARucW7jkFJeCcwAao/ZXwL3Sykvk1KmAY8CA4FLgRHAVcBtUsr9wDQgT0o5XEr5t5Z8CCFEb+AK4DMp5ZfAbGAw8K6zZOlTKeV0YEeDfYYAc4ExUsrLgTeB252rm4zPuW4+MN25zz+Bv7YkRkVRFKXlnCU+icBf0K5LlwKPA4OAaGCsq2JTlOYYXB2AohxLSvmjEGIvcI8QYhlaol3pbMxuaCfwnhAiBLABXYUQ4VLKggbbfOE85jyAumMIIR4HDFLK9xpsOwF4TkppB+zOlvzbgf+c4kf4UAhRC+iBB6WUDZ8I7JdS7nPG9EgT+04EvpFSFjrf/x+Q1YL4ioHbhRDpUsqdQogppxizoiiKcmLrhBB6wI7W0LRZCPEXYLOUsgRACLEbiHNlkIpyIirhV9qrN4DngBDgtRNs8yWwUEr5CoAQQgI+DTeQUpY1sd/VQBcgSAjxvDOBBogCHhJCTHS+9wNOp77+NillygnWNRVPQ1FAct0bKaUN2NyC+MajtTJtF0Lscr6uf3KgKIqinJErnL/Hxypv8NoMeJ6leBTllKiSHqW9+hCtTjK2qeRZCBEOxALfOt97nMKxN6I9jtUBDVvZs9Ba0IdLKYcDFwDjTif4M5AFhNW9EUIYhBADWhCfUUr5KBCD9vm+OHshK4qiKC3hHMK5n6vjUM49KuFX2iUppRm4EzjREJxH0Fq3L3S+P5URE6qdLTUTgSeEEL2cy5cC452PbQHuAJ50vq7A+fRACPGGEKLrKZzvVCwFrhFChDrf34xWynOy+D4VQvg4P9cvaOVEiqIoSvsy0PmnKGeVKulR2gUhxChgDlqZTZWUco6zo2vd+g+AJCBOCFEppZztLG2ZJ4QYx9EhO5cLIa5BG94TIcQPwBQp5R6hTeg1AfASQjwE1KDVY64R2mRec4BngV+EEDVANvAP53F3AruFEJucy7OPib8fUDfW/odCiHlSyk8arL8crcwmUgixxtmRGCHEXOfnelwIUSilXCWEeBj4ytkPoAjtxoeTxPcF8L1zHx+0TsyKoijKaWpwXQKthn+mlPJH57qG15N70a4lVwNmIcQBIADtCWy4EOLTBoeNARaepY+gKPXUxFuKoiiKoiiK0oGpkh5FURRFURRF6cBUwq8oiqIoiqIoHZhK+BVFURRFURSlA1MJv6IoiqIoiqJ0YCrhVxRFURRFUZQOzK2G5QwNDZWxsbGuDkNRFKVd2LZtW5GUMuzkW3Zc6rqgKIqiae6a4FYJf2xsLFu3bnV1GIqiKO2CECLD1TG4mrouKIqiaJq7JqiSHkVRFEVRFEXpwFTCryiKoiiKoigdmEr4FUVRFEVRFKUDc6sa/qZYrVays7Mxm82uDsWlvLy8iIqKwsPDw9WhKIqiuJS6LrQudX1RFPcnpJSujqHFEhIS5IgRIxgzZgxjxowBIC0tDX9/f0JCQhBCuDhC15BScuTIESoqKoiLi3N1OIqinCVCiG1SykGujsOV1HWhbanri6K4j+auCW7Vwh8YGMjbb7/daJnZbCY2Nvac/lEXQhASEkJhYaGrQ1EURTmr1HWhbanri6J0DB2ihl/9qKvvQFEUpSH1m9h61HepKO6vQyT87UV5eTl+fn5s27btuHWXXnopc+fObbTs5ZdfRgjBE088wb/+9S/Gjh3Lpk2bKCgoYMKECXTu3Jnly5efrfAVRXETDquDlAdTXB2GchatWrWKuLg40tPTT3nfpUuXUlpaWv/+qquuoqCgoBWjUxTF1b5N+bbZ9Srhb0Uffvgh119//XGPl/fs2UNMTAzvvPNOo+WPPvooAE888QTPPvssU6dO5Z///Cfh4eH1Cf8tt9xy1uJXFMU9lG4oJXt+tqvDUM6i0aNHExMTc1r7Hpvwf/vtt4SHh7dWaIqitAPv73y/2fVuVcN/MtOmwY4drXvMpCSYP79l2x48eJBXX32VxMRE5s6di5+fHwDLly9n0aJFXHrppfzwww8MHz68yf1zc3MJDQ1tpcgVRemoCv5bgN5fDxWujqT9c9V1YerUqVitVrp06cLq1avp2rUrBw4cYOrUqYwfP57JkycDWjKemprKQw89xJAhQ9i1axfTp08nKSmJ2tpa7rrrLiIiIujUqRNlZWUAbNq0iXvvvZeBAwdiNBr59NNPOXDgABMnTmTw4MFkZ2dzySWXcNttt7FmzRrS09OZP38+vXr1IjExkalTpzJ//nyGDx/OL7/8wtKlS+nRowe///47Dz/8MBdccEHrfmGKorQpKSU/pP/Q7DYdKuF3pc2bN3PppZcSHh7O1VdfzUcffcTkyZMxm83o9Xr8/Py47777ePvtt49L+F944QV0Oh1VVVUsXrzYNR9AURS34LA6KFpZRMh1IfChq6NRmrJq1SoOHjzI6tWrAa1F/Z577uGHH34AwNPTkwkTJrB06dL69zNnzuT8889n+/btPP/886xYsYLFixfj7+/P3LlzcTgcvP766wBcfPHF3HDDDVRXV/Pyyy9zzz334Ovry4QJE7j++uux2+307t2b2267jSuvvJLY2FimTZtGbGwsAElJSYCWJNx8881s27aNiIgIdu/eTVVV1Vn9rhRFOXP7j+wnrzKv2W06VMLf0pb4tvDJJ5/g6+vLzp07MRqNvPXWW0yePJkVK1ZQWFjIrFmzMJvNfPvttxQVFTVqyX/iiSfqnwYoiqI0p2RdCbYSG+HjwlXC3wKuuC78+eefJCQk1L/v1q1bs9t7eHiwfPlyVq9eTXl5ef2IOA2Po9Pp6hP2Or179wagf//+2Gw29uzZw/bt2/H29m7RqDpFRUWUl5cTEREBQN++fVv8GRVFgQn/NwEPnQfvXPfOyTduQydr3YcOlvC7SllZGUFBQcycObN+WXx8PNu2bWPHjh0sWLCgfnlBQQHvv/8+06dPP+XzzJ8/n2nTprVKzIqiuKfCFYXoA/SYrjS5OhTlBBITE1m/fn39+9TUVAD8/f0pLy8HIDMzs3797NmzMZlMPPnkkxw4cIDNmzfXH2f37t0AOByO4zrsNhw9Z9WqVaxdu7b+vG+88Ub9Or1ej5SS3bt3198kAISGhhIYGEhBQQHh4eEkJydTWVnJkCFDWuNrUJQObUfejvq6+QcufID+Ef1dFssP6T/Q2b8zOeSccBu3SvjLysqYPHlyowlWXK22tpZ7770XT0/P+mX79u0jODiYwYMHExYWxtSpU4mJiaGmpobKykoWLFhA3759+fPPPwFttJ4HHniAsLAwAAoLC1m2bBm5ubk899xz9cdNSVGjcijKucpebad4dTFFK4sIvT4UvZfe1SG1C+3xujB69GhWr17N3XffTffu3fHy8gLgxhtvZMqUKdhsNoqLi0lOTubXX3/lpptuYsaMGdTW1mKxWMjIyGDdunVMmjSJO++8k6lTpxIcHIyfnx9vvvkmkyZNYuPGjezatYu+ffsyaNAgLr74Yl599VUeeOABoqKiqKqqYsmSJdx5551cffXVzJ49G6vVyp133klycjLLli1j8ODBLF++nBkzZpCQkEBOTk6ja46iKCf20i8v4efph07oeHbjs6z42wqXxFFXvz+y20g+bOaxr1vNtDto0CC5devWRsv27t3bqMXiXKa+C0XpWOxmO8XfFlP430KKvizCUeXAI9yD/t/0x3+gv5ppF/e4LkyYMIEJEyaccMAGd9DevlNFcaWU4hR6LujJ9IunY9Qbee6n59h17y76hp/9sri9hXtJfDORxWMWM2ngpI4x066iKEpH56h1ULzGmeR/UYS9wo4hxEDEbRGEjwsn8LJAdAY1orK7+Pnnn+tb1AcOHIi/v7+rQ1IU5QzN/20+HjoPHrzoQTz1nszfPJ9XN73Ku9e/2ybnq7HWkF+VT15lHvmVzn+r8smvzGdHvjYM2fDY4c0eQyX8iqIoLuawOCj5voSCTwoo+r8i7OV2DCYDYX8LI/zmcIJGBKHzUEm+Oxo6dCjbt293dRiKorSibbnbuCT6Ejr5dwLgkq6XsDN/5ykdw2wzH5e8170+dlmFpekxmIO9g4nwjWBi0kS6mZofHEAl/IqiKC7gsDooXV+qJfmfF2ErtaEP1BN2Yxhh48IwXWFC56mSfEVRlPYmvzKfIV2Pdm6PC4rj95zfj9tuXeo6NmVvIr8y/7hEvqy2rMljm7xMRPhFEOkXyfmdzifSL5II34j6ZXWvw33D8dR7NnmMprRJwi+EiAeeA7YDUcARKeUzQohZwPAGmz4vpVzr3OcRIAAwAWuklF+2RWyKorSclLLRSCDKmXHYHJT+UErhfwspXFmI7YgNvb+e0BtCCRsXRvCoYHRGleQriqK0V1JK8qvyifCNqF/WzdSN4ppiysxlBHoFAlBtrWb0R6OptdcS5BVEhK+WsCdFJtW/rv/XL4IIXy2JNxqMbRJ3W7XwBwPLpZRfAAgh9gghVgFIKYcfu7EQ4kJghJTyGiGEAdgrhPhRStn07Y+iKG1u55U7MQQb6LO8j6tDcWvSLind6GzJ/6wIa5EVvZ+ekOtCCB8Xjukq0zk34o4QIhKtUWiAlHJwM9tdBqwDkqSUu89WfIqiKCdSaamk2lpNpF9k/bI4UxwAaaVpJEVqE9ttzNhIrb2W1bet5uruV7sk1obaJOGXUh77XEMHVAEIIZ4EagE98IaUshq4Ftjk3NcmhNgLXAaoVn5FcYHKXZWUrC0BoPS+UoIuDXJxRO5F2iVlv5RR8EkBhZ8VYs23ovPRETJGS/KD/xKM3vvcSvKPMRT4Akg60QZCiHDgZiD7bAWlKIpyMvlV+QBE+DVu4QdILUmtT/jXHlqLUW/kspjLzn6QTWjzGn4hxFjgOynlPiHECiBdSlklhJgCvAHcBYQDexvsVu5c1khhYSGDBh0dbWjy5MlceumlbRp/S+Tm5vLKK68QFBSE3W5n//79xMbGMnbsWB599FEsFgtXXnklAMXFxXTr1k1NoKW0OxU7KrBX2gkaGkTu4lyEp8AjxIPUR1M579fzVGlPCxWvLWbfHfuw5FrQeesIGR1C2LgwQq4JQe97Tif59aSUnwohhp9ovRBCB7wAPAJcc7biao8qKiqYNm0adrudpUuXujocRTnn5VXmATRu4Q9ytvCXpNUvW5u6lqHRQ/H28D67AZ5Amyb8QogRwAhgGoCU8s8Gq9ej/ZgDFAANxyoLcC5rJCwsjKbGW3Yls9nMddddx8qVK+natSsAFouF8ePHc8EFFzB8+HAqKyuZNWsWAEeOHGHfvn0ujFhRmrb3tr3UHKyh/+r+5C/LJ+zGMEwjTeyftJ+ilUWE3RTm6hDdQuZLmQi9oPfHvQm5NgSDnxob4TQ8DrwjpSw52Y1me20Iai3+/v7cfvvtKtlXlHYiv9LZwt+ght/kbSLQGEhaqZbw51XmsatgF7OvmH1G57LboawMSkq0v+Lio6+bet+cNrsSCSFGA5cC/wQ6CSFigBullHVJfgJwyPl6FTDTuZ8H0BvYeKrnnPbtNHbk7TjT0BtJikxi/tXzT7h+1apVxMbG1if7AJ6ennz66afHbZuXl8eiRYvqk39FaS+q9lVRvacaYRAkX52MtEk6TepE4GWBZL2aReqMVEKuC+nQQ0Naiizsvm43PRb1wK+/32kdw15tp+ynMro80IWIWyJOvoNyHCGEF9AXcDgbjQKBu4QQX0sp1x27/ckaglxxXXjvvfeYMWMG99xzD4cOHeK7775jyJAhDBkyhF27djF9+nSSkpKYM2cOTz/9NE8//TTbtm2jrKyML7/8Er1ez3fffcebb77JRRddREmDK3lFRQUPP/ww3bp1IyMjg6uuuorrr7+exx57jI8//pgpU6bw008/kZSURFBQEL///jt+fn4sWbKkVb8DRTlX1Zf0+EayezdkZkJtLUR4duPgkVRsNvh48/cA+BaM4tdfoWtXLXk/WcJ+7PuyMmhuflxvbzCZjv41p61G6RkIfAJsBTYAvsBCwCaEeA2t9b4fMAVASvmbEGKDEOIFtFF6pkspS9sittaWmppKZOTRxzoZGRl8/PHH/PLLL7zxxhsAbNiwgWnTplFdXU3nzp1dFaqinFDR50UA9FvVjz9v+hOPcA+CRgQhdIJus7uxe8xuct/JpcuULi6OtO0c+foI5ZvKyX0nl4Q3EqhJr+HgfQfp9lI3/Pr6UXu4loLlBXS5v8sJR9Ip+6kMaZEEjwo+y9G7PyGEL+AjpSwExjdYfg/wrjt12p04cSLvv/8+F1xwAbNmzeLnn3/Gx8eH888/n+3bt/P888+zYsUKHnnkERYuXMi1117L9OnTGT16NDt27OC8887jjjvuYMeOHURGRrJ48WIKCrSH3i+88AIJCQk8/PDD1NbWEh8fz7Bhw3jppZd4/fXXuf/++3n00UcJCwtjy5YtPPLIIwwYMIAjR44QEhLi4m9GUdybwwGb/8xDSB39E0IpzG+wclwcB8L34DUR7GPWQkIoD/w1CZpJ2AE8PRsn7Z06QWKi9jo4uPG6Y997eTU+VnMPRNuq0+424JSayKSUc870vM21uLSV+Ph4Nm/eXP8+JiaGxx9/nNjYWCorKwEYMWIEr7zyChaLhexs1f9MOTuklNosrQEn/7950edF+F/gT/CVwZy/5XyEXiB02i9HyOgQAi8LJP3pdCJuj8Dg3zFLVErWaK2ohZ8X0v217hx+4zDF3xRTvb+aAd8NYNeYXVTvrcZabKXb801PcFK8phhhFAReGng2Q3c7ztF3bkd7+vsUMBeYgNYQdI9zGw/gMbQW/slCiEVSyj2nei5XXBfq9O7dG4CEhATmzp3L6tWrKS8vp7CwsNF2PXr0ALSnFRUVFRQVFVFdXV3fmNStWzd+/vlnAJKTk7nrrrsAMBqNmEwmUlJSGDx4MBEREfj5aZdef39/4uPjATCZTFRUVKiEXznnSam1om/bBhkZcN55EB8Pu3bB4cPQvTuEhWnr9uyB33+H1FQwm7X9MjOhdlQ+oncoo67QM3Ik9O6tJd5Pb+rGV/mreHSGlYWe3zEg8Ape/ElHeTlkZ4PB0HTS7uPTfKLeWjrmlfssGj16NM8//zzp6enExsYCzkTLbj9uW09PTyIjI1m/fj2XX375WY5UOdccfv0wKdNS8E7wJuyvYXR7oekk1ZxppuL3CrrN1tb79vZttF4IQfzL8Wy/cDuH3zhMzBMxbR772SYdkpK1JRiCDVgOWyj7uYz8D/LxO8+Pqt1VbOmzBYCgEUFkzs4k9IZQAgYHHHeckrUlBA4NRO+jOuc2R0r5I/DjMYsXHrONFW3ozufOVlytra7/wezZszGZTDz55JMcOHCgUSNRw+3qhIaG4u3tTW5uLp06dSI1NbV+3YABAzh0SKuGNZvNlJSUkJCQ0MafRFFal8UCublaoltX7rJjB2Rlaet8fbXl8fHgd4LmYylhyxb44guoqtISaoNBO9b27dqf3a61oHt4gE6nlcmYzS2PMywMevWCoCCIiYEbboB14XlYfCL58OXG246yxvF/39TSbez7lH+Vz0NX3sIlvU7/O2ptKuE/Q0ajka+//pqXXnoJk8mE3W4nJSWFCRMmcOTIETZu3IjFYuG557RrVnV1NZ6enirhV9qUw+Igc04mvn190fvpyXwxky73d8HY+fgJPerKeULHhp7weAEXBOCX5EfZTx1zaozKPyqxFlnp/kZ3Dj10iAP3HMBaZKXXsl7UZteSMjWFXh/0wjTSxNZ+W9l3xz4G/j6w0ag7tbm1VO2qqr9xUs5da9euJSMjgwULFvDYY49x0003MWPGDGpra7FYLGRkZLBu3TqKi4spKytjyZIlJCUlkZyczLJly7jkkktYunQpkyZNYvDgweTl5ZGcnMzPP//MjBkzeOihh3juuefIzMxk4cKFBAUFsXjxYsrKyvj8888BKCsrY+nSpcTExJCRkcHixYvrr0OK0hK1tbBhg1bG4u0N6elaa7dOp7VoG41aYt6tm1aGkpEBOTnaOpMJevSAqCjtONnZsGnT0b8DB47WpgcGaueoqDg+BiEgIQH0eu1mwMdHuxGw2bQbhoICLZn39dWWWa3acfv2hVtv1eK2WLTlDocWV0QEJCVpCfz27ZCWpm0fHQ0pKXDkCMTGavF37Xp86/tFi/MJMR7fR6tuaM5nNz5LuG84oxNGt+7/IGdIyOZ6A7QzgwYNkk11zqp7bHquU9/FucVabCVtZhoxT8VgjGycyOf9J499t++j3zf9MAQY+GPoH/T9oi+h1x1N6qWU5LyVw6EHD+GT6MOgbYOOPUUje8bvoXxTORelXdQmn8eVMl7MIO2JNIbkDWHfhH0Uf1uMsauRi9IuQugFDosDnadWt1/8fTHJVyUTck0IfT7vg86gLc9+I5uUqSkM3D4Q//P8mztdqxFCbJNSNv8/XAenrgtnh/pOOy4p4YcftNZvb2+ttGXHDli+XEt+GxKi+U6kJxMaChdfrJXSREdrifjOndpxhw3TWtM9PbXkPytLK6vZsUNbHxUFNTXazYOnp3asoUNh7FjtpuFsiZ0fy7CYYXww9oNGy/cX7afXQq1J/6GLHmLuVXPPXlBOzV0T3KqFv6ysjMmTJzNmzBjGjBnj6nAUxaVK1pWQszCHiq0VJP2QRM7CHLLnZ9P1sa7kLcnDJ9GH4KuDcVQ7QAcVWysaJfypj6WSNScL01Umer9/8gu5Ty8fCpYXYK+2d7iSlZI1Jfgl+eEZ4UnojaEUf1tM5MRIhF5r2qlL9gGCRwaT8EYCB+87yL7b9xE4NJCSDSUUfVaEb39fbDHbOHRoNUFBIwgKGoZe7+Oqj6UoyjnGZtNaqW022L0bvvtOW3755VBZCWvXQmGhVvrSqxdceiksWQLrjhn/yssLxoyBiRO1mvOqKq21Oy5Oa+G3WLSW+4oK7Xw5OVqreFSUtq6oSGvFz8nRbiJCQ+GCC7Qa+ZbWq194Yat+Na1CSkl+VX6jITnrxAQdLXedeN7EsxlWi7hVwh8YGMjbb7/t6jAUpV0wp2qFiBWbK/i9z++YU80Yo42kPJACQI93eiCEQO+rxzfRl4ptR5+XVmyrIGtuFp0mdaLHWz3qO+g2x6e3D0ioOViD34DTG7ayPbJV2Cj7pYyoB6MACL8lnKo/q+hy/4lHJOoypQu12bVkvphJwfICdD46Yp+NJXByNrt2j8bhqCYr62WE8CQwcCgm0yiCg6/Ezy8JbU4pRVGU1mW1aom9s383oCXrAHXTOMTEaCU4Fgv85z+waJFWn/7GG1oLe1UVREZqre/6Ztp1vLy0v8BALck/VvfucFHHexhMhaUCs83caJbdOl4GL6ICoujk14m+4X1dEF3z3CrhVxTlqJrUGjzCPIj6ZxRpT6UR/WQ0cc/EUbC8gJL1JUT8/egPkv8gf458cwQpJTjgwL0H8AjzIP6V+BYl+6C18ANU7a3qUAl/wScFSKsk9Hrt6YfB30DC/JN3guz2Qje6PtIVaZPo/fRUWf8gOfk6jMbO9O+/lpqaA5SUrKW4eA1paTNIS5uBh0coJtNITKYrMZlG4eXVxJWyBaR0kJGh6rFBPflVlDozZ2rJ/rPPavXn0dEweLDWop6crLW09+hxtIXdYtFq2BMSQA3g1DJNzbLb0LKxywjzaZ+TVKqEX1HclDnVjFecFzFPxtDp7k54hnsCEDE+gojxjVsf/Ab6kbc0j9rsWoq/Kabi9wp6f9gbQ2DLfwK8E7xBQPW+6lb9HK6WsygH376+BFx8/Kg7AGZzBikp04mPfxlv78Ydcj1MHtjtZtLTZ5KV9QqenpH0778Gb+9YvL1jCQ6+kvj4OdTW5lFS8j0lJWsoKVlLQcFyAHx8ete3/gcGXobB0LIbqaKiL0lP//eZffAO4kRPfqWUx41+o5wed+rr15bWrIH9+7XW6/79oYvzIaCU2vJ167QOoIWFWkdSf3/Ytw/+/BPuuANmzWq+1fxUWK1abfuhQ5CXp51z9my4+2546qnjt09KOn6Zp2fHbIVvS03NstvQ8NjhZzGaU6MSfkVxUzWpNQRcoCWpdcn+ifgP0jqRlv1URvqsdAKHBhJ+a/gpnU/vpcf+hYgIAAAgAElEQVQrzqtDJfzlW8up3FZJwoKEEyaHR46soqjoMyort5GUtBEvr66N1qemPsbhw68TGXkX8fFz8PA4frpDozGSyMi/Exn5d6SUVFXtrm/9z819m8OHX0cIDwIChhAcrLX++/ufjxDHZwdSSjIzZ+PlFQektcr30NF4eXnVTzSlkv5TY7drNdnR0VqLsJSSI0eO4HXsDD/nkNJSePDBo2UxdaKitKQ+N1fbBrTvLCxMS8jLyrTymR494LnntCEk77xTK6HZuVNL2IODtaEnb79dq3M/GYcDPvlES+objNYKaDXy81037cQ54WQt/O2ZSvgVxQ05bA5qM2vxuqVlF2G/AX6gh0OPHMKSZyHxk8TTSoR8evl0qIQ/961cdD66RuVPx6qsTEan88VqLWbnzis4//wteHgEAWA2Z5KTs4hOnSbRs+c7LTqnEAI/v374+fWja9eHsNvNlJf/QnGx1vqflvYkaWlPYjAEYzJdgcl0JcHBo/Dy0jqElZVtpKJiMwkJb+KcrFw5RlRUFNnZ2cdNcKWcnNkM+fnaiC11I594eXkR1aBQu6REG97QlcrKtGRbp9Na2FNTteS6Ne/vVq6EefNg82Yt0X7qKbjnHu1c27fDb79pif2IETBgAIwapXVqbcrixXD//dpTgjrR0VBert0szJoFU6dq5TXe3lp9fFiY1nG14Wd64AF4803tfB9+qD1p6Nz56D7q/rZt5Vc5W/ibqOFv71TCf4bWr1/P66+/zoEDB7jnnntYv349Bw4cYNy4cZSXl1NQUMDbb7/N999/z8yZM/H29mbTpk2NjnHzzTeza9cunnnmGf7617+66JMo7qQ2uxZpk3h3827R9npvPb59fanaWYVplImgYUGndV6fXj6Uri9FOmSLa//bK1uZjfyP8wm/NbzZ0qaqqmT8/QcRF/ccO3ZcRlraDHr0+F8AMjKeByQxMU08Q28hvd7LmdhfAbyExVJASck6SkrWUFy8lsLCFQB4e/fAZBpFZeV2PDzCiYycgEr4m+bh4UHciTIvpVkffKCVn9x4I3z22fHr586Fhx+GJ56AZ57RSlSOHIFp02DFCu0mYfBgrRXa1/f4/T/5RBstxjlZ8CmRUmsZf/FF7Vzx8fC3v8E33xxd/vjjp37cpnz+uXbsnj3hkUe01+edp63r0kUb3eaf/2z58SZNgnHjtJlajxzRZmcNdz5k3bMHnnxSewpwrCFD4IUXtA61y5dryf60adr/DjrV//+sy6/MRyd0hHi7X6cHlfCfocsvv5zMzEy+/vprpk6dSkBAAF9//TWzZs0C4I477mDp0qVMmTKFlStXsmfPHr755huuueYaAHbv3k1GRgaJiYkq2VdarG6EHq9uLX/M7j/In6qdVcQ9e/qJkE8vHxxmB+YMM95xLbvZOBvsZjtCCHTGll8B8z/Mx1HloPM/Op9wGykdVFXtIjJyIkFBQ4mKmkZ29jzCw29Dr/cmL28JnTpNrm99bw2enuFERNxKRMStSCmprt5bX/6Tl/ceDkc1cXEvoNe3n+9f6TgyMrR/t207usxu1xL7TZu0hDo6WktCv/1WK2v57TcoLtaGcLTb4b33YMoUrQSmYYvzd9/B+PFaojpypDZiTHNsNvj+e601fc8e+PFHbQx2f3+tpfuPP7Qkv08fuOwyrQX+ssu0cd6b8/XX8NhjWglM//7a0JJVVUfXWyxaYn3BBdowliea6fVUBQRoEzwdKzFRu8EoLNRiqanRnrT8/rt2UzV8uFYWlJMDl1wCL7+skn1XyavMI9w3HL3O/YamdquE/6SjMUybphXFtaakpDMqisvLyyO0QWHerFmzmDVrVn3C/+6773LnnXeypuFzPkU5iZrUGoAWt/ADRD8aTdBlQQRc2HTn1JaoH6lnVxUVWyrwH+SPd/zZSzztZjsOswOPII9Gy5OvTEbvq6f/6v4tOo6UkpxFOfid71ffv6EpZnM6dnslvr7acWNjn6aw8DOSk6/G4ajCYAgiJuaJ0/9AJyGEwNc3EV/fRKKi/onDUUtV1W58fQe02TmVc1tdwp+RobVEp6drHTt799bGVo+K0hLtzz/XLo2ZmTBwoJZ4D3D+Z9m1Kzz9tDYp0t13a8tSUuCWW7TE9dAhLWlduPDoeX/5RSslGjJEO+bKlbBsmZbkgtaqfvHF2o3C3/52dLjJI0e01+XlWgv8rbdqNyshIVon2unTtVKbiRO1xFpK7elEfj58+eXRunyjsfHNyeDB8NVXrZfst0RYmPZXZ+BA+J//gY8/1r4Lm0177eFx4mMobSurPIvO/iduJGrP3Crhd5dx+Pfs2cOsWbMoKytj9OjRjBs3rn7dNddcw9NPP81XX31FdHQ0vXr1wtOz+Q6XinIsc6oZYRAYo4wn39jJp4cPPj3ObBIon97a/ntu3oPD7EDvr6fn4p6Ejzu1DsCnQ9olyVcnYy2wMvjPwfV9ECp3VlL2U5n2elclfv1OfoUu/62cql1V2hwEzRS9VlYmA+DnpyX8BoMfvXotITV1BmFhNxEZOQFPz7b/7HV0OiP+/gPP2vmUc09GhjYpk82mJc6rV2utyeHhWgv0J59onU4nTtT+mvKvf8Gvv8Lkydq/iYlauYrBoJXfPP88vPuu1iIfFqYl4HPmND6GXg9XXaXdFIwceeLEu244ycBALRm+7DKtpn7ePK0sydtbuzGZN087txCwa5c22dQdd2hPJgID228S7eOjlT+dTgmU0vr2FO5hWMwwV4dxWtwq4T+pdtI9PTExsb6kpylPP/00Tz75JEOHDmXOnDl89NFHZy84pUOoSa3BGGOsnwn2bPEM9dRa+fUQ/Xg0OW/msOfmPdjKbHS+u+lWD1uFDYP/mf/UZDyfQdmPWmJfc6AGn57azUfu4lyEUSB0gsOvH6bnOz2P2zd7QTaBlwTif57Wmp/zVg56f/1JRyqqqkoGBL6+feqXmUyXM3Dg5jP+PIrSHmVkaAnz2rWwdatWx3/VVVpreEvp9VoL/XPPaYm21Qp/+Qu89prWqfXxx7Wyn7rZX7Oztc6wt9+udZANDYXRo4+24rfUhRdq5TrXXae16nfurJUbGY3aTcNtt2l1/506HS0tasnIOIoCUF5bTlZ5Fn3C+px843ZIVYG5wNVXX423tzd9+/ZVrfvKaTGnmU+pnKc1Dd49mMG7BhP590iSfkwi8LJA0p5Mw1ZhO27bgk8K+Nn0M3n/yTujc5b+XEr60+mYrtKGBjnyzREA7DV28v+TT9hNYUTcHkH+f/KxHrE22rf4u2JSHkgh41mtVsFWYaPwk0Iibos46Y1IZWUy3t4J6PVN9D5U2oW6Us+vvvrK1aG4PYdDK6cZMEBLjN99F7Ky4KabTv1Yfn7auPB792q1/6tWaSPQgDaO/SOPaKPKDB0K//0v/O//auU8Dz6oJf6nmuzXGTlS6yswYoTWot+1q/Z04tNPtdr8LVu0zrbGlj8cVRQA9hXtAyAxLNHFkZwelfC3EiEEKSkpfPXVV+zbt48lS5Y0Wr9u3TqSk5OZN28eAD///DN33313o31WrVrlitAVN2RONZ9Sh93WJPSivgxG56Ej/uV4rIVWsuZmNdquMrmSfXfuAzsceugQ1lJrU4c7KWuJlb3j9+IV50WfFX3wSfSh+JtiAAo/K8RWaqPTpE50eaALDrOD7Ney6/d1WB2kTEsBoGRdCQ6rg5I1JTjMjhbNQ1BVtbO+nEdpn+pKPdUsu2euoABqa7XOtAMHasNPGgxai/npio/X+gAcWzk3e7bWF+Djj7Wa/NZ06aWwfv3RPgWg9R348EPthuAf/2jd8ykd1678XXyy+xNAK+cBlfCfk77//nsASktLiYyMpHv37nz22Wfs3r2bO++8s9G2V1xxBdu3b+ehhx5qtLzhPqNHjz5rsSvuy1Zuw1pkdVkL/7ECLggg9KZQsudmYymwAGCvsrP7xt0YAg30+6Yf1iNW0v+VXr+PvdpO3rK8+hk8pV1SfaAaaW88o6eUkv1378eSayHx40QM/gZCrgmh9MdSrEesZL2chXd3b4KGB+HX14/QG0PJeDaD1BmpWAotZL6YSfW+aiInRmIvt1P+WzlFXxZhMBkIGNJ852WbrZKamkP1HXYVpaOr67Bbl/ADXHGF68fcby1jxmilSkGnNyqxcg56+deX+fvnf6e8tpw9hXsw6o3EmdxzyF+V8J+BhQsXMmPGDDZs2MB9993n6nCUc0TdCD1ece1n5stuz3fDXm3n8ILDABR+Xoj5kJleS3sR8pcQOt/bmcNvHqZqrzb2Xc7bOez7n32U/lha/35Lzy38EvoL++/eX5/4572fR9FnRcQ9F0fAYC1BD74mGGnVOvBW7aoi/tX4+icOiR8n0ukfncicncmv4b+S/u90gv8STPy8eNBD8TfFFH9TTPA1wegMzf/8lZf/Bkh8ffu1xVemKO1Ow4R/8GDt9emU8yhKR5FSnILNYWND2gb+LPyTnqE9Mejcs/ure0bdTnz++eeuDkE5B1Xt0pJm38T2U1fu09MH0ygTee/nETsrloKPCjBGGzGN1JoGY2fGkvO/ORR8VEDcs3Ec+UqrwS9dX4ppuIkjXx/BGGUkcGgguYtzCbgkgPCbw0l7Mo2AiwLo+kjX+nMFXhKI3l9PxdYKOt/bmdBrj/a603nq6PG/PQgeFYw504xXnBfBVwWj99YTcFEAh988jL3cTuiY5nvq2e1mUlIewGiMck6IpSgdX8OEv18/bXKr6693bUyK4kopxVpJ6HeHvmNP4R4uirrIxRGdPrdq4VedsxQFKv+oROetw7tn+yjpqRM5IZLazFoK/ltA8Zpiwm8Nr5+N1zPck6DLgihYUYCtzEbZRm20ndINpTgsDkp/LCXkuhB6f9Qb/0H+pM9K5/Drh7HkWIh7Ma7RrL46Tx2hY0Px7etL/Cvxx8UhhCDspjC6PtiVsBvC0HtrE6QEXxWMvdyOMAiCr26+R2B6+kyqq/fRs+e7GAwnHqdfUTqSjAxtiMrAQK3m/q9/bb/DVSpKWyszl1FUXQTAl/u/JL003W1H6AE3S/hV5yxFgYrtFfj29z1pScrZFnpDKPpAPQfvPQh2iBgf0Wh92F/DqNlfQ9a8LKRNEjg0kPLfyilZX4KjyoFppAkhBHEvxFGbUUvqjFSCrgjCNPz4AuJe7/Vi4LaB6H1aPtth8FVakh94WSCGwKYfbprNmRw4cC9ZWa/QqdNkgoOvPIVvQFHcW0bGyWe/VZRzxaGSQwCM7DaSwxVauaq7dtgFN0v4FeVcJx2Syj8q8T+//bU66730RNwaga3Uhk8fH3z7NS45Ch0bCgIyX8zEEGIg+olopE2SPisddBA0QutJZxppImh4EEiIe7bpzlFCJ9B5ntrPl/9Af4KvDqbLlC5Nrq+pOcSWLb3JzX2Xzp3/QXz83FM6vqK4I5sNxo3ThrBUCb+iHFVXznPf4KN9NN054Vc1/IriRsxpZuzldvzOO4vzvZ+CyImR5CzKIWJ8xHEz2Bo7aTX6ZT+VEXJNCEHDghAegorNFfhf6I9HkFY7IISg55KelG8qJ/DiwFaLTegF/VefeMSdvLylOBxmLrhgDz4+x0/epSgdUVGRVqtfN7HWZZe5Nh5FaS8OFR9t4e8e3J2M0gziTceXkboLlfCfoY0bNzJz5kzS0tI4ePBgo4m0HnvsMZYtW8YzzzxDz549eeedd4iPj6ekpISDBw/yxRdfkJqayqOPPsqBAwcYN25c/b779u2jpqaGhIQErFYrr7/+OtOnTweo31c591T8UQHQLlv4QRuic8CGAQRc1PSQl2F/DdMS/mtD0PtqHWnLfiqr79xbxzvOG++4tuujUFT0Nf7+52M0arMDS+kgL28ZJtMoley7mbq+XWPGjFHlnqfBbNb+dTi0GXFVC7+iaFKKU4j0i8TP049/DPwH23O346F3304tKuE/Q8OGDWP48OFUV1ezePFipkyZAkBBQQFbtmyhc+fOTJo0iUGDBrFkyRL699daGKdMmYLD4aBHjx7ccMMNfP3118yaNav+uCtWrKCyspKJEydSWVnJ66+/ziuvvALAe++9d9Y/p9I+VG6vRBgEvn3bzwg9x2qq5r5O5J2RSJsk9AZtlJygy4MaJfxWazHFxd9iNEbh69sfD4/WHzC7uno/u3ePwcsrnvPO+wmjsRNlZT9RW5tBt27Pt/r5lLZV17dLOT11Cf9LL8Fvv8Ff/uLaeBSlvThUcojuwd0BeHjIwy6O5sx1qIR/2sGD7KisbNVjJvn5Mb9uPvBmzJw5kylTpnDXXXdhNBp58803mTJlCi+99BIAkZGRzJs3j6eeeoru3bvz5ptvnvBY999/PwsWLDjh+okTJ576B1E6hMo/KvHp44PO6J7dbwx+Bro+dHSIzc6TO4MDAodqpTtpaTPJyVkIgIdHGIMH78HT8+gQmna7GYslDy+vmONKhlqqsFAbTtdiySU5+Ur69FlJXt4y9Ho/QkNvON2PpihuqUab1oPYWHjwQZeGoijtSkpxCiO7jXR1GK3GPbOGdqhv375cfPHFvP322+Tn56PT6QgLC6tf/8EHHxAWFsaoUaPo2bMnr732WqP9d+7cybRp05g2bRplZWVnO3zFDUgpqdhe0W7LeU6HsbORuGfi0Bl02O01FBR8SEjI9SQm/hertYisrJfrt62pSWXr1v5s3hzHpk1RZGa+dFrnLCr6HH//QfTr9yXV1QfZsqUHeXnvERp6E3p9+31yoihtoa6F37t9jfKrKC5VY63hcMXh+hb+jsCtWvhPVqvZkpb4tvTvf/+bq666iszMTB577DF2795dvy44OJg5c+YwZ84ctm7dyk033UR8fDzXXnstAAMGDGD+/PkA7N+/3yXxK+2XvcZO/rJ8rAXWdtth90wVFX2OzVZKVNQDmExXcOTIlxw+vICoqAcxm9PYvftGpLTSrdtLFBR8TGbmS3Tt+ugptfTX1h6momILcXHPYzJdwUUXHSI//2NKStbQtetDbfjpFKV9qkv4vdrPxN2K4nKpJakAKuF3lfZeq5mYmMiwYcPw9PQkNLTxTJ6jRo1izZo1CCEYNGgQ/fv3x2KxNHmcnj1Vp0HlKEuBha3nbcWSY8G3ny9hfw07+U5uKDf3Xby8YgkKGgFAbOws8vM/ZseOYdTUpGA0xtC//w/4+vbCYAjmwIG7qalJwcen5Tf6RUX/B0Bo6FgAjMYuREc/THS0+9dnKsrpUAm/ohyvbkhOdx6V51hulfC3R1u3bmXjxo1UVlby4osv8uGHHwJQkF/AewvfIyczh8XPLSY+JJ7br7+d2IRYyq3lxMXFcf3115OSksKXK7/kQOoBFi9ezKRJk447R13L/7x583joIdUKea7Jez8PS46Ffl/3I/ia4NOuXW+PpJTU1BykunovpaXriY19GiG0SkNv73g6d/4HubnvEh09g+joGfWz3gYEXAhARcUWfHwSSEl5mNraDLp3fx2jsdMJz1dY+Dne3j3x9e3d9h9OUdyASvgVpbEaaw0b0jcAqoVfaWDQoEGsX7++0TLpkPiV+7HgsQUY5hgQBsHt4nbsVXbs5XZ8evug99VmCI3rFMf7M9/HEGI44TCETz31FE899VSbfxal/ZFSkrckj4BLAggZHeLqcFpdXt777N+vdUIXwpPIyAmN1nfv/hrdur2AwdB4PH5f30R0Ol/KyzcTEnI9hw8vQMpaSkrW06vXUkJDjy/5y8p6ldLS9cTEPNlmn0dR3I1K+BXlqAVbFvD4949TZa2iT1gfTN4nHnXO3ahOu23AnGHGXmbHGG3EO9YbrygvjF2MeHXzQngIzBlmpJQAWHK1sh7bERuOWocrw1baofLN5VTvq6bTnSdutXZnhw8vwMcnkQEDNnDhhSl4eUU3Wq/TGY5L9gGE0OPvP5Dy8i2UlKxBylp69FiEl1ccu3ePpbDws/pt7fYqDhy4n0OHHiI0dCzR0U+0+edSFHehEn5F0bz080s8sPoBhsUMY+3ta/njH3+4OqRW1SYJvxAiXgjxsRDiESHEa0KImc7lwUKIt4UQjwsh3hVCRDTY5xEhxLNCiAVCiOvaIq6zwVZhw3bEhmcnTzzDPRut0xl0GLsacVQ7qM2uxV5jx1ZqwxBqAHE0+VeUOnlL8tD56Aj7W8er2y8v30pl5Ta6dJmCyTQcL6+uJ9+pgYCAC6ms/IPCwhUYDCYiI+8iKWkDAQEXsmfPLRw8OJX09GfZsqUXOTkLiYp6iD59/oter4Yj6UjqBnP46quvXB2KW1IJv+LObA4bE7+YyO+Hfz+j43y25zMeX/c4t/a9lS9v/ZKR3Ua69SRbTWmrkp5gYLmU8gsAIcQeIcQq4G7geynlf4UQY4BXgNuFEBcCI6SU1wghDMBeIcSPUkq3GJ/SYXOAHYSnoDazFuEp8Ozk2eS2BpMBjzAPrPlWrEVW0IGxixGhE1gLrXh28nTbMdaV1mU32ylYXkD4uHAM/h2v+i439y10Oh8iIv5+Wvv7+1+AlBYKCv5LRMR4dDoDOp0//fuvZs+eW8jNfQeHw4yfXxKJicsJDLyklT+B0h6098Ec2juV8CvuLDk/maU7llJrq+Wjmz5qtE5K2eI+bz9l/oSvhy/Lxi5Dr9O3Ragu1yZZhJTy2FstHVAFjAbqprL8BXjf+fpaYJNzX5sQYi9wGfBlW8TXmqSU1ByswVHlQBgFslbiFe+F0DX9H5kQAmO0EZ23jtqsWjzCPdB56PCM9MRaZKX6YDU+Ce47sZLSemoza7FX2Am6ovVnm3U1m62M/PyPCA+/tcmSnZao67gLDkJCjj4UNBgC6N//G6SU2O2V6PV+Haqjs6K0JpXwK+5sU9YmAL4+8DW1tlqMBiMAmWWZJC5M5Lu/f8cl0Sdv7EkpTqF7cPcOm+zDWajhF0KMBb6TUu4DwoEK56pywORs0W+4vG5d+LHHKiwsZNCgQfV/7aFVx1Zmw1HlwBBsQAiBIciAIaj5+yghBJ7hnvj298UYpf3HqfPU4Z3gjbRKqvdVY6u0nY3wlXbMUqCVeHlGNP20yJ3l5i7G4aimc+d7TvsYRmMUnp6RCOFJcPDVx60XQmAw+KtkX1GaUTfTrtHo2jgU5XT8mv0rABWWCtanHR1A5Y/cP6iyVvHF/i+a3M9it/BD+g/17+sS/o6sTRN+IcQIYARQN2F3AVA3TWgAUCKltB2zvG5dwbHHCwsLY+vWrfV/kydPbrvgW0BKieWwBWEUeMV54dvXF+/u3i1OMHQeukbbGvwN+PT0AQE1+2owZ5mRdtlW4SvtnDXfCnBcXxB3IqWkomI7Utrrl9nt1WRmvozJNJKAgEGnfWwhBOHh44mMnFA/XKeiKKfGbNZa99V9seKONmVtYnTCaPw9/Vm5d2X98rTSNID64TWPteSPJYx4fwR7C/did9hJK03r8Al/mxUGCyFGA5cC/wQ6CSFigFXAxUAWcInzPc5/6zr2egC9gY1tFVtrsRXbcNQ4KPEu4d8P/5ugoCDsdjv79+8nNjaWF198kUWLFrFz504iIiJITU2lc+fOzJ49+4TH1Pvo8e3jS212LdZ8K7ZiG55dPPEI8VAtleeYuhZ+jwj37TiUk/MWBw/eS6dOd9Ojx1sIIcjJWYTVWkBMzL/P+Pjdu89thSgV5dxVl/ArirvJr8wnrTSN+wbfh7/Rny/2f8EixyL0On39TLnbc7dTZi4j0Ktx6ejGDC3F3Ja7DR8PHyx2S4eaZKspbZLwCyEGAp8AW4ENgC+wEHgCeEkI0QOIBx4GkFL+JoTYIIR4ATAB06WUpW0RW2uxVdgwZ5ix6C3cNOEmVq5cSdeu2igjFouF8ePHU15ezr/+9S8KCgoQQmCz2bj//vtPemyhF3jFeGEIMVCbVUttei06Tx2GgI7XcVM5MUu+M+EPdc+Ev7o6hUOHpuPhEU5u7jsYjV0JChpOZuZLmEwjCQoa6uoQFeWcpxJ+xV1tytbq9y/uejHRgdEs372cX7J+YVjMMNJK0zDoDNgcNn7O/JnRPUY32veXrF8A2JG3g05+2rDXqoX/NEgptwF+J1h99wn2mXOm5z047SCVOyrP9DCN+CX5kTA/odEyW7mNmpQadEYd63evJzY2tj7ZB/D09OTTTz+ltrYWKSXz5s3jjjvuIDQ0lEWLFrX43AY/A7oEHVU7qrBX2dsk4S/9sRTpkJhGdJzJJToKa4EVQ4gBncH9OnBLaWffvjsQwoOBA7eSmvoY6ekzAW2CrdjYZ1wcoeJKQohI4DlggJRycBPrhwNT0QZz6A/8KKVcfFaDPEeohF9xV5uyNuGp9+T8TufTN7wvAsHGjI0MixlGakkqV8RdwYb0DWxI39Ao4c8uzyazLBPQEv6EYC3H6+gJv/tlEi5mr7FTc0hL9r17eJOemU5kZGT9+oyMDGbPns2YMWPIzc3lxx9/ZMeOHfTu3ZuhQ4fy7bffntL5dAYdwlPgqGmbSbkOTDnA/rv2108EprQflgKL23bYzc1dQnn5ryQkvIGXV1d69XqP3r0/ol+/b7joonQCAy92dYiKaw0FvgBOVKfoDcx2NgRNBRYIIdT1qg2ohF9xV5uyN3F+p/PxMngRYAwgPjieHXk7kFKSVpJGn7A+XBx1caPOuQC/ZmkdfZMik9iRt4ODxQcx6o10Cejigk9x9nSoGpFjW+LPlJQSR40DS74Fh8WBzkuHJc+C0Am8u3uj89ARHx/P5s2b6/eJiYnh8ccfJzY2lsrKSvr27cuyZcuw2+2sXLmSG2+8kaysLEJCQloch85b1yYJv63cRvXeapBQc7AGnx4+rX4O5fRZ861u2WHXZqsgLe0pAgIuqR9jX6czEhFxq4sjU9oLKeWnzlb8E61f3eBtd2C/lLLJH8HMzEzCwsIIDAwkKCiIyZMnu3xAB3eiEn7FHf1++He2HN7ClMFT6pclRSbxR+4f5FflU6leyLUAACAASURBVGOrIc4Uh7/Rn2c3PkupuZQgL22I618yf8HHw4cJAyYw7btp/JjxI/HB8eg6eJtCx/50Z8BhcVBzsIbqPdXUZtViLbBSm1GLtEi8473rx8kfPXo0aWlppKen1++rjf+tjUpy1113AaDX6xk7dixep/HLqvPW4TA7kI7WbYWv2FoBzkMWry5u1WMrZ85SYMEjvH3U71dXH+DgwWmkpj5FZeXuZp8IZWa+iNVaQPfur6qO5soZcc7SvhD4x4m2iY6OprCwkJSUlHYxepu7UQm/4i4+2/MZb219i/d3vM/lH1xOZ//OTLtoWv36pIj/Z++8w6Mquj/+me2b3isJSSihd/wJIuBrQVSw4IuCoviiothQxIIFsKOIFQuKvWFBFAQrClgRkB4ChBAgvfdkN7vz+2OSQEwP6dzP8+yz2blz5567m2S/c+bMOYOIy45jR8oOAKK8o/hP5H9wSiffx31f2e+3o79xWuhpDA9V0YRbkrZ0+g270ME8/BUl1CdMmMCECRNa7DplBWUUHygGCeYwMwZvA8KoimqhUznzKzCbzaxZs4ZFixbh7e2Nw+Hg4MGDTJ8+HW9vbzIzM7nzzjvx9PQkPj6eRYsWNcq7D6C36rFLO85SJ3pr8xWFyNucB4Ap1ETWt1l0uaNLs42tcfLYUts+pKesLJ8DB24lNfV9hDAhpZ0jRx5HCBNGox9+fpcQEfEwJlMgAPn52zh6dAmBgVfj4VEtNFtDo1FIKR8RQiwFtgshhkopq6Vr1jg5NMGv0RHYlLCJyz+7vPJ1H/8+/DDtB0LcQyrbBgUNAmDVvlWAEvw9fHoQ6h7KezveY3LfyRTYCtiesp37Rt1H/4D+CAQS2enj96GDCX43nVuLF9uSTknJ4RKEQaiKt5bj4l5YavZWBgcH8/zzz9d4bNWqVSdtk86qbHAWN6/gz/8rH2sPKz4X+JD8ejKOYkezjq/RdJylThy5jjb18BcW7mXPnkkUFe0nLOxuwsLmAIKMjC8pLj5ESUkCSUmvk5r6HmFh9xAQMJlduy7CZAogKuqk9+BrnGIIIVwBFylluhDiZuB9KWUBkAWUoYoxaoK/mSkpgUb6oDQ0WpWSshKuX309EV4RfHf1d6QUpDA4aDDu5qr1VyoFf6zSXRFeEeh1eqYNmMYzvz9DakEqfyf9jUM6OCPsDNzN7nTz6XZKFN2CDib4SxNLm3U8R7EDW5INc5i50mtvS7EhSyTWHtYqYr8tqbCjOeP4pZTk/ZWH99ne+I73JfGFRLLWZeEodKB31eM7wRedsX3c/6mILb1tq+ympa1g374Z6PWuDBz4E97eYyuPhYQcj64oKorl0KF5HD78MIcPP4xe787gwb9hNgfVMKqGhkIIMQaYhqrR8iDwLDAd6A/cBNiAl4QQsUAk8LaUcncbmdupKS4Gq7WtrdDQqBkpJfN+msf+zP38MO0Hevr2pKdvzxr7hriH4Gv1JaUghRD3ECwGtXR17aBreeq3p3h96+u8s/0dIr0iGRMxBlCTBE3wt0MchSpDjrVbw/87SadE6Kp75qWUlMSX4CxyIowCS7gFZ4kTW7INg7cBg2f7eWuETqCz6HAUO+rv3EBKE0uxJdtwP80dzzGe6Kw69kzaU3ncGGjE2t2KI9dB+APhBF4Z2GzX1qiftqyye/jwoxw+/DAeHiPp2/dTzObaMxe4uETTr98X5Ob+xtGjzxEaOgs3t/6taK1GR0RKuQHY8K/mpSccXw4sb1WjTlG0kB6N9sqR3CPcsPoGvo/7nhuH3Mg5UefU2V8IwaCgQfwU/xNR3lGV7b38enFa6GnM/2W+St153UZcjCpJyeCgwXy+93NN8LdHUj9MJeLhiAb1Lcstz5fvqsMUaMLgZajcRGhLseEscqKz6rCn2zEFmiiOLwadittvb+isOhxFzSf48//KB8Dj/zzQW/SEzQmjKLaIkFkhOAudJL+VTFl2GUWJRaR9mKYJ/lamsspuK4f0FBXFcvjwQvz9r6B37/fQ6Ro24fD0PANPzzNa2DoNDY3mRhP8Gu2FrUlbcUgHp4Weht1h56x3zyK1IJWlFyzlpmE3NWiMCsEf6RVZpf3agdeyOXEzc0fOZVT48aKPM4fOJMgtqMoEobPSoQS/3k1P6oepdH2oa73ZPyrFvkWHtEtK4kowBhqxhFkoKyjDlqQ8+eYuZgp3FVIUW4S0SSxRliqbctsLOquOsuwybBk2pE1iCjLVuHLRUPL+ykOYBG4DVX20yEer/nH4XqiCOvdetZfcTblNN1yjSdjTyj38rRzSc+jQfej1LvTo8WKDxb6GhkbHRRP8Gu2FWWtnsT9zP/tv3c/q/as5lH2INVPWVKuSWxcVcfz/FvAzBs/AxejClH5V00P7uvjyv8H/O3njOwAdSvAbfY0U7y8mf2s+HsM8au3ntDmV2Leq4lhCL1RqzVQ7OMGeaUeYBOZwMzqjDoOfgbKMMgy+Bow+7SMN4r/RuahJSOlhtY9BZ9Zh9G2arSkfpJD4UiKeZ3hWphetDbeBbqR9lIY9y95u35vOiC219T38OTmbyMhYRWTkY5hMAa12XQ0NjbZDE/wa7QEpJfsy9pFXmsc9P97DpoRNDA0eygU9LmjUOMNDVHa43n69q7SbDWamD5reXOZ2SDqU4Dd4GxDJgrSP0uoU/LY0G0hUvnyDErTmMDNOmxN7uh2dRU0EKjalmkOV8DcFtV+PpsHTgCXSgs6io/hgMfZsO0Zfo7qnTHu9nuCCnQUce/4YxQeKyf01F88xnvT5pE+913Ub5FZ5vvdY72a5F436saXZ0Fl16F1bL2tSfPw8TKYQunS5s9WuqaGh0bZogl+jPZBamEpeaR6h7qG8s/0dAFZdsarRtVyi/aLZeuNWBgYObAErOzbtL3alDvIK8kjyTyJhZUKtfaRDYk+3Y/A2VPFeCyGwRloxh5mxRlurhO3ojDrMoWaEvvEhMhs3bmTs2LF07doVm81W5di9995LSEgIjzzyCNOnT0ev17N3794qfdatW4cQgnnz5lFWVsYjjzyCEILXXnutSr/MzEx8InyYdsM0EnITcOQ6kA5J6ZFSbIk27On22t8TKdl37T7SP0tHOiVdH+zKwB8GNmhDaEXIT8H2goa+JRrNgD1VTeJaq3BVTs5GcnN/JTxchfRoaHQUKuqzrF69uq1N6XCUlamHJvg12prYjFgAXjj/BULcQxgUNIiJ0RObNNaQ4CHodVqK8X/ToQS/p6cnI28ZiT5Bjy3DVmMfe4YdHNTorRd6gSnQ1KzpJkePHs3YsWMJDAzkzTffrGxPS0tj8+bNhISE8PDDDzN9+nSGDh3KggULqpz/zTffADBv3jwMBgMPP/wwQ4cO5cknn6S09Hga0qVLlxIeHs7UqVOJHhINEkqTSinLKQOd+lk6aq5+mvdnHgXbC+i2uBtDfhtC5KORDX4PTIEmjIFGCncUVrY5ihz8M+Yfjj53tKFvk0Yjae0quwkJj2M0BhAcfH2rXVNDoznw9PRk2bJlLVqMsbNS8RWjCX6NhhCfHU/E8xEs+WNJndXW6+PjXR9zw9c3YHccd1TGZirBPzRkKNtu3Mb6a9ZrldqbmQ4V0gPgNcYLgNyNufhf5l/l2IEDd5Cbug0hBLr9zSPq3dwG0aNHzUW1TuThhx9m1qxZzJgxA7PZzCuvvMKsWbNYtGhRZZ8ZM2bwxBNPsGvXLvr378+aNWu48MILWbp0aZWx+vXrR1FREW+++Sa33HILaWlpCCHw91f3q3fVI0xC7UnQg7WHleJ9xZQVltVoW+LSRPQeegKualpctttANwp2HPfwZ3ydQe7GXHI35lJ6rJRuz3Q7qQ3E7REpJSUlh7BYohr8T8fpLCM19T1AR3Dw9JO6vj3N3mrZovLy/iY7+3uiop5Cr9cScjeErCzw8Wnda5aUwNatsHMnnH029Kw5FbWGRoMpKVHPmuDXaAjrDq4jITeBOd/PYUvSFl698FU8LZ6NGkNKyUM/P0RcdhxWo5UXx78IwP7M/VgMFsI9w9GJDuWL7jB0uHfVfZg7OhcdOb/kVDsmHYAThLH1xWe/fv0YMWIEy5YtIzU1FZ1OVynQKzCbzdx3332VXv5169Yxfvz4GsebP38+ixYtoqSkhKVLlzJr1qzKY0IIDN5qrmYKMmFwM2AMMOIocLDtjG1krMmo7GtLs5H+WTpB1wZhcGva/M5tkBuFewpx2lXhr7QP0zB3MRN6WyjHlhwjbUXnK36ZmPgSf/3Vnc2be3P06BKczqqTKbs9k+Tk5Rw4cAd7905h//5b2bp1MLGxM4iNvZ7i4vgmXTfj6wwKYwqxpdbv4bfZ0ti3bwZpaZ9is6USF3cPf/wRwebNfdm+/Sz27fsfR48+S3HxIez2HI4cWUxs7E3k5Gyq9M5IKYmPfwiDwYuQkJubZPOpxrx5EBICsbEte53kZPjiC5gzB0aMAA8PGDUKZs2C3r1h6tSWvb5G56dC8GuFtzQawqYjmwhxD+GJ/zzBJ7s/oc8rfVgZs7JRY2xL3kZcdhz9Avrx0uaXePuftwHl4e/h00MT+y1Ih/Pw60w6PEd6krNBCX5HoQNHiQO9RU+E7zOUFpTi0s8FvaX147fmz5/PuHHjOHLkCPfeey+7d1cvDDljxgwWLVrEwoULmTix9vi0vn37MnLkSBYuXIjVasXPz6/KcVOASYUulcfhm7uYMSQbKEwpZPeE3fR6txeB0wI5/MhhpE0SMiukyfflNtANaZMU7SvCFGwi69ssutzZhainokh+I5n8rfkETuk8efpttjTi4x/C3X0YOp2FuLg5ZGV9S58+KzAavcnI+JrY2Buw29PQ6VwxmYIoK8vEaAygZ8/XOXDgNo4efZqePV9t1HUPLzzM4QWHQQCy/qJbx469SErKW6SkvFXeIvD1vQghTNhsKWRlfUtKytvExd2NEGakLEWns5Kc/DoeHiPo1+9rcnLWk539Hd27P4/BUPtGeA3F+vXw1FMgJSxdCi++2DzjlpXBrl3w++/HH4cPq2NmMwwbBrNnw8iREB0N77yjrq+hcTJoHn6NhiKlZFPCJs4MP5P7z7yfc6LOYeaamUz6dBL/zPynMh1mfXyy+xMMOgPrr1nPfz/7L3N/mMvVA64mNiOWgUHaRtuWpMMJfgCvsV7EPxhP4muJ2CPs2I7ZVFXYAgfCIOpNNdlS9OnTh9GjR2MymaoJ9ApMJhPz5s3jvffeY/78+XWON3/+fEaNGsX+/furHdOZdVgijv+XFjqBwcPAaTGnsfOCncTOiCXtkzSy1mURMisE116uTb6vEzfuOn51IMskgVcFInRChRPFFjd57PZIfPwDOJ1F9Or1Pq6uvUhOXs7+/Tfz118qr29ZWQ6urgPp33817u7DEP/ySOTnbyU5+S26dn0IszmEsrJ8YmKuwuHIJyxsLj4+46uFCSU8nsDhBYcJvCYQc6iZpGVJuA93r9VGp9NOcvKb+PhcQGjoLHJyNhIYOA03t35V+pWUJJCW9imlpUcICvofLi49SUl5n7i4O9mx42zs9nTc3IYSGnprM717nZesLLjmGhVK07evEt2PPw6vvw4PPww6HQwaBOvWgXvtH13lWH/+eVzcb94MheXbZIKD4Ywz4PbblcAfNEiJ/hNZtAjuvRd8fVvkVjVOEYrL/3Vrgl+jPo7kHiExP7GyaNXw0OGsvWotwc8G89Ohnxok+J3Syad7P2Vct3H4u/oz+/TZXLriUn449AOHsg/x3z7/benbOKXpkILfc4yKGTtw8wE813lSlleGdEgcBQ50brpW3eixZcsWNm7cSEFBAU8++SQffvghAOnp6bz//vskJyfz7rvvsnHjRnJychg1ahQ33ngjN954IwBLliwBYPHixSxYsIAlS5awc+dOfvrpJ84++2wyMzMB+OKLL0hISGDFihUMHDiQsLCwGu3RmXT0+6If/5z5D1nrsoh4JIKuD3Y9qXu0Rlsx+BjYf9N+DB4GXPq64DpATSCsPa0U7iysZ4SOgZROkpPfJDl5OV263Imray8AgoNnYLVGk5z8BgaDBy4uvQgOvqHWwlTh4feWh/vcTmjoLA4dmkd+/hZMpiB27bqQ4OAbiI5eVtk/989c4h+MJ+CqAHq91QuhF0Q9oSYXTmcpQlTP1pOR8RV2eyqhobPw9b0QX9+aC5NYLF0JD59bpS009Cas1m7s3j0Rp9NG//6rEULLaFAfH3wAiYlKnDudsHIlTJ+uns85B3r1gldeUUL97bePn+d0wv79Vb33MTHqmF4PAwfCddcpcT9yJISHQ0P+hbX2HoKGIoSwSik7lxegk6J5+DUayq9HfgWoUqU2yC2I7j7d2XhkI3NGzql3jD+P/cmR3CM8dtZjAIzrNg43kxuLfluEQzqI9otuGeM1gA4q+D2Ge1TmKDf6GlUxrSw7slSi929d4TJs2DDWr19frd3f35/ly5dXvr722mtrPP+uu+7irrvuqvV1BZMmTWLSpEkNssngaWDQL4Mo2l+E5+mN21BTEzqDjsGbBnP02aOkfphK+LzwSgHqEu1CxqoMnDZnu6xQ3FDKynLZteticnM34OU1loiIqqsvXl6j8PIaVcvZVbFao+jSZTbHjj1LRsYXCGGiX78v8PG5gAMHbiM5eVn5hKI30ik5eNtBTMEmer7as0pqWJstjb/+6o7F0pXAwGspK8uiqGgfPj7jSE39GLO5Kz4+5zfpfn18zmXQoA2Ulibh7j60SWOcaqxfD1FRMHy4CukZNkyJ/WHD4OuvVRy0p6fy+vfurcJ0fv8d/vhDefQBvL2VqL/6avU8fDi4Nn3xrU0RQoyu5dA04IbWtEWjaWiCX6Oh/HrkVzzMHvQP6F+l/czwM/kq9iuc0skvh39hzvdz+O7q7whwrZ4k5MuYLzHpTVzc62IArEYrE3pO4OPdHwMQ7asJ/pakQwn+inzLEyZM4PQPT8fS1cJRy1EoBVuSStOpd9M8lQBGH2OziP0KXPu40mt5L6KXRVfZ6u0S7QIOKD5UfFJhQ21NUtIb5OZuoGfP1wkOvuGkV4m6d19MePi95OX9gdXaDVfXvgBERj5GWtqHHD68kL59PyH5rWTyt+TT+8PeGNyr/jlmZa3D4chHSgeHDs1FCAMmUzAZGV+Wj/X4SXnmPTxOa/oNnmI4HLBhA1x+uXotBDzyCCxcCJ99dnzT4/z58P33KtwGlPC/9NLj3vuePVXoTyfhOWAHatfJifSvoa9GO0QT/BoNZdORTYwMG1ktv/3orqN5e/vb7E3fy6LfFrE9ZTuPbXysMvvOiWxL2cbAwIF4mI/vF5vcd3Kl4O/pq6Uea0k6lOCvyLd8IiJGYPA0UJZVBgL0Lprgb0n+XZzMJVoVaSqO7biCX0pJSspyPDxGEhJy48mN5ZTIMonOpMNk8sfPr+rGbJPJj9DQ2ziS8BR/3X0Jxd8G4XmmJwFTqntDMjPXYjIFM3z4HkpKEjCZAtHpLOTm/kZW1jpCQ285KVs1Gs727ZCTA2eddbxt/Hj1OBGjEb75Bv75R3n+22vYTTNxu5Tyt383CiHOaE0jTnQEabn4G4cm+DUaQkZRBnvS9zCl35Rqx84MPxOAd7e/y/dx3+Nr9eW1La9xx//dQTefbpX9pJTsSNnBJb0uqXJ+RViP1WDF2+rdsjdyitMpfE0GLzVv0bnoOl0++PaONVq5Notii9rYkqaTl/c7RUX7CA6ecVLjSCnZe8VeNrlt4p8z/yH57eQa+4X4zoYiK8W3zsTzow+I+MhZrY/TWUZ29veVG3yt1gj0eitCCLy8RhEV9TgGQ/Ot4GjUTUXU3omCvzb8/eG88zq92KdC7AshTEKI24QQd5WL/QOtaYdWeKvpaIJfoyE8suERBIIJ0dX/xqK8owh2C+a5P59DL/R8e/W3GPVGHvr5oSr9UgpSyCzOZEDggCrtVqOVW4bfwmW9L2vRe9DoLILf0wA6qoVEaLQ8Ri8jxgBjhxb8ycnL0evd8PeffFLjZKzMIP3zdLzP86Ysr4zY/8Vy7OVj1foV/qqHu5bgaf0PeSHvsePgMDZv7k1W1o+VffLy/qSsLAdf3wtOyiaN5uHnn9Wm3ODgtrakXfIc4AOEA8lA3enHNNoNmuDXqI9tydtY+vdSZg2fVU2sg6oLNLrraBzSwUU9L2JYyDBuP+12Pt79Mcn5x51eO1N3AtQ4xlPnPMVrF73WcjehAXQSwS/0Ate+rpiC685brtEyuES7tIngd5Y5G1TeOzNzHVlZP2K3Z2G3Z1FUFEtm5jccO/YChw49SFraCgICrsRgcGuSDQW7CiiOK+bArQdwG+xGv1X9GLplKH6X+HHwtoMcfuwwtnTbcXvWZaI71puBZ3zBiBGJ9Oz5GuAkJmYqNls6AFlZaxHCgLf3OY22SaMqUsIzz6g0mE3BboeNG+E//2leuzoRh6WUC4FkKeUh4EhbG6TRMLTCWxp14ZRObv7mZvxd/HnsP4/V2m9M1zEAzBw6E4DL+6jNTj8f/rmyT4Xg//emX43Wo9O4xNsq977G8Uw9rUVu7h/k528h6blsdPF9GfTexehcJKmp7+F02vH0HImra3+EEGRlfceuXXV5yXWYTEFNykPvtDvZed7O41WfddB/TX90BvW72OeTPuyZvIfDD6mCWuH3hhP5WCRZa7Pw/o83OrMOE/6EhMzEw+MMtm4dwoEDt9Gr13IyM1fj6TlKC9tpBtauhXvuUTH1mzc3LOXlifz9t8qRrwn+WokSQpgBKVRRippzBmu0OzQPv0Zd7E7bzebEzbx64at4Wbxq7Td90HSC3II4v7vKGjcoaBBeFi9+jv+Zqf1VSfCdaTsJdQ/F10UrHtJWdBrB35YkJyfzzDPP4O3tjc1mY8+ePZx11lncdtttACxfvpwXXniBnTt3Vjlv4cKF5OTk4Onpyd69exk5ciSzZ8/m008/5Z577mHkyJEsXryYkJAQFi9ezNy5c3nggQd47LHaZ9ptgTXaij3Djj3LjtHH2Ozj5+dvo6hoH76+E0hNfY8DB24HnDABcAr+eu8cjEMTKSreW3mOj894oqPfIjZ2Ji4uveje/QUKCnai0xkxGHyxWqOwWntgNPpWK5zVUA7efpCcX3KIeCQCg7cBS7gF96HHKy7pzDr6f9Wfgt0FJDyawJEnjmCJtFASX0LY3KqayM2tHxER84mPf5CMjJVIaadHD62U6slis8Gdd4LJBFu2wK+/wplnNm6Mzz9X52uCv1a+A+IBCdwI3Nm25mg0FE3wa9RFhVe+woNfG1ajlUt7X1r5Wq/TM7rr6Goe/prCeTRaD03wnySlpaVMnDiRL7/8ki5dugCQmprK5ZdfXin4t23bRklJCX/88QcjRowAYPfu3Xz11Vds27YNgOzsbJ544gkAJk+ezCuvvMLUqVMJCQkB4O6772bu3Lncd999rX2L9VKRqacotgjPEc3rkS4q2s/27f/B4chFCBNS2vD1nYDH3/OJf2gfHo/+Rl7k2zjTfel3+le4uvYjPf0LDh26n82be+BwFDB48K94ep6Bj895zWJT8aFiEh5PIOWtFMLuCSPioYg6+7v1c6PXW73I/zuf/TNV1WSf8dV3dIaF3UNJyRH0ejd8fS/Ay0tTmCfLSy/BgQNKtM+cCc8+qwR/TAx066aEfF04HPDJJ3DBBSqHvkaNbAP6AN2Bg1LKnDa2R6OBaJV2NepiZ+pOzHozPXx7NPrcsyLO4uvYrzmae5QgtyBi0mMY3318/SdqtBgdSvDXl35t9uzZbN++vVmvOWjQIJ5//vlaj69Zs4aIiIhKsQ8QGBjIypUrAdi+fTunn346PXr04PXXX68U/N7e3sTFxfHOO+8wadIkvL29eeaZZ5rV9tbCtY9Kx1nwT0GzCP7U1I/JyFiFr+8EEhIeQ6cz0rv3KrKzf8JkCiY8/B52z9uLxdSNwddNZf9tN5P8ZhrucaMw+5kJD5+Lq2tf9u69gtDQO/D0bHyWQHuOnaRXkgi4IgBrt+MBrknLkth/836EQRB6R2hlRdz60Lvq6flqT3aevxOXXi5YI6oHzep0RqKjX2+0rRo189138OCDSqxPmqRSaz7+OIweDZs2wWOPwQMP1D3Ghg2QnAxTp7aOzR2Uj4D7pJS/trUhGo2jpERVezZ0KCWg0VrsTN1JH/8+GHSN/wU5K0KlNPv58M8MCR6C3WnXPPxtTIf6M68pD399SGlHSgdSOtHpzCdVqKgmDh48SFBQULV2f39/AL788kvmzZtHSUkJPXr0ICcnBy8vL0JDQ1m3bh2LFy/mrrvuYvjw4Tz66KOcdtrxYkhvvPEGP/74Y7Wx2xuWKAuWKAuZazMJnRVa2e60OclYlYH/JP9q+ftro6BgF/v2XQc4SU//FCEMDBz4I15eY/DzU9X5HEUOsn/MJviGYIQQhM/pTvJrmRx97ijdF3cHwNf3AkaOTEWna9putNR3U4l/IJ74h+MJujaIqKeiKEko4cCtB/A+25te7/TCHGJu1Jg+43yIfCISa5S2Q66l2LsXdu9WIv2ee1Thq3feUcduuUV5+GNjITxcVcmtT/B/9BG4ucFFF7W46R2ZZUCYEOJVYDPwkZSytI1t0mgAJSWad1+jdnam7uS8bk1bGe8f2B9fqy8/H/4Zfbnu0jbsti0dSvDXx4meeCkdFBcfKg8FMZYLfhMuLn2qVFF1Ou3odFXjzh2OEsrKsjGZguqtuNqlSxe2bNlS47HCwkK2bNnCk08+CUBYWBjvv/9+ZajPyJEjWblyJaWlpSxbtowLL7yQtLS0ymvecMMNXHSC0njhhRca8W60HkIIfC/yJXlZMo4iR2Xxs6OLjxL/QDw9l/Uk5IaQesdxOIrYu/dKDAYvhg3bTnFxLEIYcG7pQ1pmGgGTVXGq7J+ycZY48Z2gNv9YI60EXBlA0mtJdJ3XtXIfgV7vgqPEwZ7L9hB8pjhMfQAAIABJREFUfTD+l/k3+J7y/szDFGzCf7I/Sa8kkfFVBnqrHlOQiT4f98Ho27S9Cl3v79qk8zTqxuGAJ5+EBQvUz6A26X733fF8+EFBSuz7+MDLL8N998GxY3DC4hxSqgnC00/DkCGqiNZll2lZTOpCSvkegBBiNfAS8CRQ3QvSQmiFt5qOJvg1aiOjKIPkguQme+V1QseYiDGs2L2Cj3d9jNVgJdovupmt1GgMnS61jdNpx2ZLo6goBocjF7M5HDe3gVgsETidxdjtaZV9bbY0Cgt3YLMdb5NSUlISj82WSFlZVr3Xu/TSS9m/fz+JiYmVbbGxsUycOJGPP/6YxYsXs2DBAhYsWMBzzz1XuULxyy+/8OijjwJgNpuZOnUqdru9ud6GVsf3Il+cJU6y12cDYM+yc+RplZ3vyJNHcNqrF5c6kdLSJHbtuoiior307v0+ZnMQXl5j8PAYSezMWPbP3I90qBScmWsy0bvr8RpzPGtA+L3hOAudHF1ytMq4aR+lkbUui9jrY7Gl2mgoeX/l4XmGJz2e78Gwf4bh0sMFW6qNPp80XeyfymRlwfXXw9GqHw9SqmMny3XXwUMPweTJsHOnitH/44/qxa/CwsDVFSaWF0Bevfr4sQMHlLj/3//AbFaThdxcuOaak7evMyOEWC6EeBHYCaQBp7fm9bXCW01HE/watbErdRdwcl75awdeS5R3FDcPu5lN123CpNdSp7clncrD73SWUFgYAzjQ6axYrT0xGDwAMBi80Os9KC1NQq/3AASlpccAHaWlR9DpTBgMXtjtGTidhYAOmy0Fg8GnTi+/i4sL69atY8mSJXh4eGCz2UhJSWHOnDnMnTuXwMBAevfuDUBiYiL79u3j/vvvZ8aMGWzevJl7770Xi8XC/v37effddxFC8MUXX5CQkMCKFSsYOnQowcHBlasXFROI9obXaC/0bnoy12Tid5EfR54+giPPQeTjkcQ/EE/aR2kEXVuz0y89dRWxsdfjpJhevd6hZGVf9v22j+jl0eRtzqMkTqWSKNhegNsQNzLXZOIzzged6fh81a2/GwFXBXB00VH8LvbDY7gHUkqOPX8MS6SF0sRSDtxxgL6f9K33XmypNkriSwi9RYUnufZ1ZfBvg7Fn2TH5af+wmsJHH8Hy5bBrl8ppbzZDZqYS0z/+qNp79mza2Onpavxbb1WbdBtCr15q0+7q1XDVVercDz9Um3ifeQbuukutFMTFqb4adTIKWATcI6UsaWtjNBqOJvg1aqOuQlkNZWL0RCZGT2wukzROkg4l+O32zCqvpXRQVpZPWVkeer07xcWHAXBx6YNe71KlrxACszmcoqJ9FBXtRQgjIHBx6U1JySGKi+MwGDwpKytAr3fDaPSnpCSesrIcjMaa03PYbKk4HIUEBgaxePHiasc3b95c5fWUKVOYMmVK5evVJ7oXT2DSpElMmjSpStvs2bOZPXt2jf3bAzqzDu/zvMlck0nGVxkkvphIwNQAwu8PJ/2zdBKeSCDw6sAqsfwORzFxcXeTlPQKHOhBdLcPcEvrw9ZZW5F2ic94H3I25CBMAmmTlasHtiQbvhdVz+Xb46Ue5G7MJebqGIZtG0be5jwKdxUSvTya0sRSDj98mKzrsvAZVz1Dzonk/ZUHgMfpHpVtQic0sX8SrFoFXl4qD/6NN0KPHrBsGaSmQlkZvPee2kTbFD79VInzG29s+DlCKC//0qVw1llqVeCuu2DOHBX6A6DTaWK/gUyVUm5tayM0Go8m+Ds/V35+JRLJistXAHA09yiuJld8rHV/D+5M3UmAawCBboGtYaZGK9Aigl8IEQQ8BgyUUg4vb5sO3ARUeICWSynfLz92NTAYcABxUsoaU5WUlBwmMXEpoaG3YLOlExMzFadzDsXF+9HpXHE6C7FYIquJ/Qr0eguurn0pLT1KWVkWFksUer0Fq7VHeQhPHuDEbO6KTmdBiCRstmQMBq9qXn6bLY3SUhWfUFaWhcHgg8XStdk3Bbc0xcVxgA6rNRIpJTExV2O3pxMUdC0WSxR2eyZeXmc2qACU70W+ZKzMYPclu7H2sBL1eBRCCMLuCSNmagy5v+fidaYKwyko2MXevVMoKtqDfu0UHM9PJ87VhilkLwZvA0ZfI/EPxGPPtuN/mT8FOwrIWZ+Do9ABAnwuqP7PyuhtpNe7vdhx9g42992M3qrH6GckYGoAQidIfiOZI08fqV/w/5mHMAjchjS+8q5GdbKy4Jdf1CZaux0q5sannQZffqky6bz/PjzyiBLZjeWjj6BfP+jfyJXnCRPguedU+M9XX6lsPhpNQgohtgGpwIdAgZRyVRvbpNEASkq0/SmdmezibD7f+zkO6eD6wdcT7RfNgFcHYDVaWTl5JSPCRtR67s40LW9+Z6OlPPyjgK+AQf9qv1JKefjEBiFEF+BuYLCUUgoh/hZCrJdSHqhmrMGTAwdu5ejRZ8vFth5f38cxGoOw21MwGLwxGOoWczqdEas1CimPi3OdzojFEoGUEpCVhZjM5hBKSuIpKTmMxRJRKfrt9ixKS4+g13tisURgt6dhsyVTWFiI1dqt2oTD6SxFCEO7mwykpn5IbOz16PWeDB++i5yc9aSlfYTB4Et29g+V/dzchjBkyO/odHVnpfG/zJ/sH7LxGedDwFUBlRVnfc5Tn0nuRiX409I+JSbmGgwGL7rpVxL3jDeRj0Vy7MVjFO0tou+XfdEZdey6SMUQBl4diNHPSPJbyZQmleIxwgOTf83edu+zvOm3qh9JryWR/UM2EQsi0FvU+x5ycwjx8+Ip3FOIa1/XWu8j7888XAe6ore2r8+ro/LNN8oDf+mlaiPs+PEwYAD4+anj11yj0l5u2KC87Y0hPh5+/11t2G0sZ54J99+vhP+I2r/3NOrnRuBSYIqU8gMhxNOAJvg7AJqHv3Oz7uA6HNKBh9mDOd/PwdvqjUM6cDG6MOadMXw++fMaQ24cTgd70vZw07Cb2sBqjZaiRQS/lPJzIcTYGg7dKoRIAVyAl6WUWcA4YKtUahvgD2A8UE3wFxR4sn69BV/fbNLT/eje/UYCAtyxWLpgMvkjhLHerDoV1CS+1bnHzzcafXE6S7HZkigpEVgsYTgchZSUxKPXu2G1dkMIHWZzKHq9ByUlhygqisFs7loeHpSF3Z6J01mEEAbM5giMxtrLU7cWNls68fEPkZz8Ou7u/0dBwT/s2zedwsKduLkNZsiQzeTl/YnDkUdpaSL7999IXNxcevR4sc5xDZ4G+nzUp1q70deIaz9XcjblYEn9nZiYq/H0PIO+fT8n4e5chDmJ0NtC8bvUj/xt+fhf4o+UEs/RnhTFFuF9njdOm5PElxMp3FlI5JORddrhN9EPv4l+OEudCNPxzzP4hmAOLzxM4suJ9Hy15oBx6ZDkbc4jaHqrJRnp9KxaBaGhMHSo8uD/u2LtxReDu7sK66lP8Eupxnv0URUOVJFh58orG2+XwQDlte40To6DUsoEIUTFzvzMOntrtBtKSsCl5gVxjU7A17FfE+gayPPnP8+UL1Q48buXvMtFPS9ixPIRLPptEROjJ+KUTmZ8PYMr+17JuO7j+PHQjxSXFXN6l1bdf6/RwjRI8AshHgHWAl2B54AXpZRPNfJaG4BvpJTpQogLgM+As4EAIP+EfnnlbdXw9/fnkUeqpsCMiYlBSlmv97mpmEzBgBObLYWCghzAiU5nwWLpXrkSAGAwuOPi0oeSkkOUlh6mtDwLtU7ngskUSllZFiUlB3E4/DCbwyonHFI6sdszMBi80OmaHiN+fL5UN+npK9m37zocjkK6dLmTqKhFJCa+TFzcXQD06fMJOp0BL69RlecUFe3l2LHn8fY+Fz+/pmXC8BhjJaX0ObJj3sHLazT9+69Bhwvpnx3E90JfDB4GDH0MlUW8hBD0+7IfZXll6Iw6lZFHABL8Jvg16Jo6c9X4EJOficApgaS8l0Lkk5EYvapn28nfko+z0Fklfl+j6RQXw7ffwvTptYfruLio7DorVqh0ma61L75w220q7r5HD+WV//ZbOPdciIhoCes1Gkh/IcSVQLAQ4iJUxV2NDkBxcfVMVhqdA5vDxrqD65jcZzJX9L2CFXtWEOwWzLQB0xBCcFX/q1jwywJSClLYn7mfd7a/w6aETcTcEsNLm18i0DWQS3pd0ta3odGMNDRi1ial/BO4DRgI1PGVXDNSyngpZXr5y/XAGKFUbxrgfkJXj/K2BmGxWMjMzGyw4G0sarNvF1xceqPXu6LTmbFae6CrofKcChfqidkchskUjItLX1xd+2A2B+Pi0rs89CiDwsK92Gyp2O1ZFBXtpbT0CDZbUpNtlFKSmZmJpZ61WSklcXH3YLGEM3z4Trp3X4JOZ6RLlzsIDJxGWNjdNValjYpahMUSoTbXNoHCwn1kXXw58qq38DZcSv/+a9DrXcnZmIMtxUbAFTXO7zD6GCsr0hp9jLgPdccSacGlT9NdUqG3heIscpL4YmK1Y3mb89g1cRcGbwPe59S8UVujcaxYAUVFcPnldfe75hooKFAx/bURFwevvQYzZqgCW59/rvYHfPNN89qs0WjuByYC5wJXAg+1rTkaDUUL6em8bDi8gbzSPCZGT0QIwZdXfMkrF75SGQVxSa9LkEhWx67mvR3voRM64rLjWPDLAtYeWMvMoTO1NJqdjAaH9AghooG0cg99UWMvJIR4EnhISlkG9AAOSykdQojvgNuEEKI8rGcEqnhLg+jSpQvHjh0jPT29/s7Ngg6Ia2DfvGotTqceuz0VKZXgFEKviks5szCbixockvRvLBYLXU6sIFSTNXl/UlISR3T027i6Hg+9EUJH797v1XqeTmfC3/+/HDv2PHZ7TqPCkgoKdrNjx9lgAeY+ic+VV6Ef5YqjxMHBOw9i8DXge2H1jDs10eudXjjtzia/RwDuQ9zxm+THkUVHCPpfEJYu6tuuMKaQ7WO3Ywo2MWDtAMxBLbNidCrhdKoUlwMGwNixdfcdNQoiI1VYz9VXwxdfwFtvqVSeFVlzHn8cjEYVzmMo/89l0r6P2hwpZRIwFUAI8R/gPKD2fyjNjFZ4q+logr/zUeYs4/ejv7P4j8VYDVbOiTqnxn79A/oT5R3Fx7s/ZmvyVq4ecDUx6TE88esTGHQGZg6b2cqWa7Q0DRX8BpRXfkr5ku3QujoLIcYA01BLvA8CzwIpwKtCiHigP3A1gJTymBBiMfCcEMIBvFnTht3aMBqNREbWHdPd3pBSYrOlUlx8ADe3geTmbmLXrovo1+/rJofMNITU1A/Q6Sz4+1/W6HP9/C7j6NFnyMr6Bh+fC9m58zx0Ohc8PUcQFnY3RmN10V5ScowdO85CCCMDh6xnV1oOuRtzCZsdRtycOAp3FNJ/TX/0rg3bHFvXRtvG0O2ZbmSuyeTQvYfo86Ga+CS9moR0SAZvGow5RBP7zcHatcoT/8EHKg1mXeh0MG2aEvM7d6oUm1lZMGYMfP895OerycBtt0FwcOvYr9Ek0oAptKLgryi8pdF4NMHf+Zj1zSze2PYGeqHnvlH3YTXWnIZJCMEl0Zew5M8lgCqSZXPYGP/heC7vczkh7iGtabZGK9AgwS+lXAAsOKFpTT39N6Bi9k/khTr6fwB80BBbOgMqTCgIs1m5Lr29z8Vg8CEt7WN8fM4nPf0zfHzOx2g8+eBKp7MMp7MInc5CWtoKfH0vrixG1hg8PE7DZAohPX0leXl/kZ+/FTe3wRw58hR6vTtdu86rdk5m5mrs9gyGDv0HV9deeJ4ZQ+bqTPb8dw/pn6cTdndYg737zYk10kr43HASHksg5KYQ3Ie7k/pBKn6X+mlivxGkpakwnLAw5Xn/N888o45Nntyw8aZNU6k5zztPVbh9/XWYO/d4fL7FolJ7arRfpJS7hRCH29oOjYahCf7Ox+603ZwWehrfX/09npa602lf0ksJ/jCPMMZGjEUgePXCVxnffXwrWavRmjQohl8IESGE+EII8a4QYrIQYmRLG3YqoUJmLicj4yt27PgPMTFXsX37GEpLU0567EOH7uG33/zYuXM8ZWWZBAVNa9I4Qujw87uUzMxvSExcSkjITIYN24Kra39ycn6p8Zy8vD8wGgNxcxsIqPScZdll5GzMIfSOUCIfb7uVmfD7wjF3NRN7Qyzpn6ZTll1G8AzNddxQ/v4bunZV1WpdXGD+fJVBB2DfPrjwQlVRd86cmicDNdG9O5xxhsq+c+utysv/22+wcKHKl//bb5p3vz0hhDitlkMts6FKo9nRBH/nIyk/iWjf6HrFPsDIsJH09O3JzcNuRid0CCG4adhNdPXq2gqWarQ2DQ3peQB4ETgLlV95EfB7Sxl1KhIYOJXk5GXk52+ja9eHOXr0WbZvP5Pw8Pvw8hqL1dqt0WM6HMUkJ7+FxdKVvLzfMZmC8PY+r8k2+vtfRlLSUgwGXyIjVVlUL6+xJCcvx+m0o9NVVXZ5eX/i4XF6Zcx9wJQA3Ie5Y+1urVJxty3Qu+qJXhbNznE72T9zP+ZwM95naxt1G0JKisqpHxgIDz0E332nPPPJyWCzqRAeV1fl4b/11saNPWeOGmPhQvW6Xz/10GiXLBFC1PQ9cDqgJTxt50ipFd7qbEgpSS5IJtitYZ4RvU5P7K2xLWyVRnuhoYJ/n5RygxDi/6SUtvJc+hrNiKfnmXTv/iJeXmfh5tYPH59x7NnzX2JjrwcE/ft/g69v45bZMjJW4XDk0rPnSlxdByBlaTVR3jgbR+PldRbBwTdWhht5eY0lMfEl8vO34Ol5vHqRzZZBcfEBgoNnVLYJncAluv0kffY5z4eg64JIeTuFoOuCELq2nYS0NypSy5rNShz8849Kg/nBByq+/vffYdAg+N//lLd/8WIlHm6/XRW08vdv/DUvvVQ9NDoEdqCwlnaNdk5Ojvq71gR/5yGrOAubw6bF32vUSEMF/wAhxOmARQjRjzbKs9yZszEIoaNLl9sqX3t6jmTEiGMUFcWya9eFxMc/iI/P+Y3KUJOS8hYWSwReXmOr1AxoKjqdgUGD1ldp8/QcDUBOzi9VBH9+/l8AeHi078Id3ZZ0w+BlIPSW0LY2pV2Rl6fy3B86pMJsjhyBA+Vb6QcOhE8/VWIf1Ibcp5+GceOUNz5Iq1l2qnCPlPLvfzcKIepM6lDeJwh4DBgopRxew/HpqJWCOGAI8JKUUltVbkbefVc9jxvXtnZoNB9J+Sq9tyb4NWqioYJ/EfAmMACVcu36FrOoDk61bAxCCFxde9G16wPExs4gK2stOp0riYkv06PHy5WbfmuipCSB7OyfiIiY3yxivzZMJr/KOP6uXe+vbM/L+xPQ4+4+rMWu3RwYvYx0X6LVCToRKeG66yA2Vj3//bfafDt3rqqKG1BD2QQh4Jyas79pdFJqEvvl7VsbcPoo4CtgUC3HQ4HZUsoSIcT/ob5/+jfJUI1qOJ2qgN3IkTBkSFtbo9FcaIJfoy4amqVnL6Bt1G0jAgOnkZDwKPv334zNloyUZej1bvTu/U6t5xw79iIgCQy8tsXtqymOPzf3D9zcBqDXN08qTY3WITERFi2ClStViM6cOW1tkUZnREr5uRBibB3HHz/hpQ4oaHGjTiG++w4OHlR7bzQ6DxWCP9hdy26gUZ2GZul5WQhxXfnPM4UQDUy0p9Ec6HRGwsMfoLT0KN7e5xIScgupqe+Sl/cXhYV7SEp6g7Ky/Mr+OTmbOHbsOYKDr8dqjWhx+7y8xuJ0FpGd/QMAUjrIz9/c7sN5NKoybx6Eh8NLL6m4/LvuamuLNE51hIphvAOo9bcxPT2dYcOGVT5OpVXgpvLSSyr0btKktrZEozlJLkgGaPCmXY1Ti4aG9GRLKd8GkFK+LoR4BPi05czS+DfBwTNwde2Du/tpOJ3FZGSsZNeui7Hb0wEnCQmPEhX1FCZTCPv2TcdiiaJbt+daxTZv77Mxm8PZvfsSwsPvpbQ0EYcjHw+PEfWfrNEu+PZbePJJmDJFef26a1FOGk1ECHGWlPLnZhhHAM8A70gp/6itn7+/P1u2bDnZy50y2GzKwz9njlapurORlJ+Et8W71mJbGqc2DRX8xf96XdbchmjUjRACT08VVaXTudO9+/PExFxDaOht+PqO5+DBu4iJuaq8t47Bg3/FYHBrFdsMBk+GDdtGbOyNJCQ8hk7nQmDg1U2q6KvR+mRnw4wZ0LcvvPWWlpdbo3EIIcYDNwNugADCgUbnERZCuAIuUsp0IYQeeA74ojxD3CQp5RfNafepSnKyiuHv2bOtLdFobpLyk7T4fY1aaajg9xdCvAwcRP0j19KutTEBAZPx95+E+l6EYcP+IT//bxyOYiyWMFxcolvVHqPRl759P6eoKAaLJQK9vv2k39Som9tvV1Vzv/5aE/saTWIeMBvIQAn+ejcOCSHGANOAYCHEg8CzwHTUxtybUJ79S1AZ4kB972iCvxlITFTPoVpisk5HckGyFr+vUSsNFfxzUJl5BgA7gOUtZpFGg6kQ+6Cq9Xp6ntGG1lRkFerTpjZoNI4vv1R59efPh6H1JlPU0KiRf07MzCOEeL++E6SUG4AN/2peesLxu6gjbl+j6Rw7pp67dGlbOzSan6T8JMZGjG1rMzTaKQ3N0uMEKndCCSHGAd+1lFG10Znz8GtotDbp6TBzJgweDA880NbWaHQ0hBAPl/8YVC7yD5a/PhPQkrS2UzQPf+fEKZ0k5ycT4qaF9GjUTJ2CXwjxtJTyHiHEz4CsaKaJMZonS3vNw796tSpMNGuWykeuodEReOghyM2F9evB2PQCzBqnLgOA1UDCv9q1fPntmMREFbrn7d3Wlmg0J5lFmdiddi2kR6NW6vPwf1v+vA148YT2WS1jTsfjwAG44gooLoZt2+C11zTxpNH+KS2FFSvU726/fm1tjUYHZbaU8pgQwiSltAEIIfyA9fWcp9GGHDumwnk051TnoiIlp7ZpV6M26szDL6Ws+MctAE8pZUL5496WN639kJio4pxLSqq2OxyqEqnZDLNnqwwn552nJgGNYeNGyMtrPns1NOrj++8hJ0el4dTQaApSyvJocO47odkIPNYG5mg0kMRELZynM6JV2dWojwYV3gL6ALta0pCWICYGRoyAW2+F3bubPs6sWTBtGvToocqRp6aq1Ga33AK//QYvvgjPPQdvv628/P37w8KF1ScINREbC2PGaEWONFqXTz4BHx84R4u01mgiQogBQohrgEFCiGvKfx4HeLSmHRV7u1avXt2al+2wVHj4NToXmuDXqI+GCv4/APeKF0KI2S1jTvOxfTuMHq0E9ZtvKhF+880q9KYhyPIdC/v2qXSFV16pvCK33grBwdC1Kyxbpsa8+mrVd/p01f+yy2DBAnXN5cuVDenpEBen0h9WjA3wxhvq+d134fDhZrp5DY06KCqCr76Cyy/Xws80TgpvIPKE50igC7CkNY2o2NulJXKoH6cTkpI0D39npELwB7kFtbElGu2Vhgr+/wEpQoh4IUQ88FAL2nTS5OXB2WeD1Qp//aU8GnfdpeLrTzsNjh493re0tOq5+flw/fUQGAgbNsDixWqD04svwh9/wI4dKoXhHXfA/v3wyitVYyGDg+Gjj1TIhBBqrF69ICBAVS8NDAQ3NzVuSQm8846amOh0qtKphkZLs2YNFBaqSayGRlORUm6QUi4EbpRSLix/PAZoZW/bKRkZqtKuJvg7H8n5yfhYfbAYtGIqGjVTb1rO8iqKB6WUXU9om9GiVp0k69ZBVhasXKnCcACefRbOPVdtUpwwAX79VYXhPPaYWgGYNk2F4/z3vxAfDyEhMG6c8sbPmAH+/mqcAQPUoz7OPVd5+2Nj1UShqAg8PFRWlG++gblzVZhRZiY8+CCsWqW8/fPmqdUDDY2WYO1auPFG9Ts2enRbW6PRkRFCjD7h5xNTg0wDbmh9izTqoyIlpxbS0/n4K/Evevn1amszNNox9aXlfBGVes1PCHGFlHIFgJSyTQpvNTQP/6pVSqCPGlW1/fzz4dNP4YILoG9flUozIECF4lRk2PH3V579Pn1Uv23bmh5fr9NB797qcSLXXw9nnqnCeCIj1WpEr15q0+9116nVAUNDS6JpaNSDlGrSuWwZvPceDByoJsN6ff3namjUwXOoQoz/zveipeVsp2g5+DsnCTkJbE3eyqJzFrW1KRrtmPpCenRSyrHAIGBky5tTNw2J1bTZlBdz4sSaBc24cSo858gRuPtuOHhQbex9/nkYNgy2bFFi3NdXCf+YGBWK05xYrUpwde8O99+vJgZhYWrC8fPPqq21+OIL+OGH1rueRuvhcKjUm8OGwRlnqM/61lvVRvPIyLa2TqMTcLuU8n9SyutOfAB3tLVhGjWjVdntnKzatwqAS3td2saWaLRn6vMjpwFIKcuEEJkVjUKIS6WUX7aoZU3kl19UDP/FF9fe55ZbYPLk42E6a9eqMJtJk8BkOt7PYml+sV9BeLjaA3Bi/P+118Lff6v4/rPPVisSLUVZGcyZoyY/XbuqMCYhIDtbbeR0c2u5a2scZ906cHFR4TXNkRfbZlO/w3l5KkZ/3Tro2VNNJq+6SvtcOxUn7v5vk8vL3yp+FkK4Ar7lL88FfqvxJI02JTFROZgCA9vaEo3mZOW+lfQL6EcP3x5tbYpGO6Y+D/84IcTTQoingfNO+PnOVrCtSaxapQRUfekGK8Q+qNj6KVOqiv3WoCaBt2QJREWpjcEt8X2+dq2aSISHK7E/dCgkJKiVDClh5Ejl/X3jDbXqERkJi7RVwhZh0ya48EIYO1ZldNqwoWnjHDgAU6eqz9RigdNPV4/vv1dpZGNiYOZMTex3Ktavh0GD2toKAIQQdwG/ogpufQxc27YWadTGsWMQFKSFjHYm0grT+PXIr1zW67K2NkWjnVOf4LcBheWPH0742d7CdjWJ7Gwl+M8/X4XNdERMJrj3Xti8GX76qfnso0WcAAAgAElEQVTHv+8++OcftYLw2WfwZfk6zdq18PvvaqOxi4va2Hnnneo9ffpptelYo/Gkpqo0eP8mP1+t6FRMrkpL1WbyxtaL+OEHlXlq7Vo1WZs7V03cCguVd3/WLOXR0+gEJCaqTRgTJ6o/4PZTrS9ISjkYeENKeQbwclsbpFEziYlaOE9n4+vYr3FKJ5f21sJ5NOqmvnn+PVLKv//dKIQY2kL2NJmEBBg/XqUdu+WWtrbm5Lj2WnjkEXj8cfi//1OCsWfPkw/5iIuDXbvUKsKdJ6zR9OunxGFcnBL7u3erLEZeXipEZOxYlWr0+utP7vqnIlOmqH0hX36pNBqo9/nuu9Xv7MaNKr7+/POVcL/wQpVKNqgBqZR/+UX9zvfurWpFaHH5nYysLPUh//STesTGqnY/P3j0UfVL1D48GwXlzxW1WqJb8+INTeagoQR/dKt+OhotSZmzjOf+fI5o32gGBg5sa3M02jl1Cv6axH55+9aWMadp5OaqjDz5+SqMYezYtrbo5DCb1Xf5nXeqcCNQ93XuufWfW1SkinzVlNrzq6/U87/3N1xwgUpRum0bXHIJuLsrIQnKWzxgALz0kkpP2hxx5qcKdrvKjlNaqt7PSy5RIv/vv5XX/fHHldgH5XVbs0b9Ht9yi9pg+2+khK1bVSSH06mKvoWHq5UZd/fq/TU6GEVFKs5r/Xol8LdtUx+6q6va5HHDDWrWOGBAe1u26SKEmAAcFULEAXtb8+IVyRw06ufYseOOB42Oz2tbXmNv+l6+vOJLhPblrFEPnSKS76GHlOfijz+UR7wzMHOmKhDm7a2ceScK/ry84xOBX39Vm29HjwYfHxV3n5MDO3dW9+SsWqW0QlRU1fbx41XYTk7O8arBFQgBt92mtMbGjTBmTMvcb2dkzx5VXO2VV1TIzV9/qU3g8+er1ZJ/p8YbMkRlaHr4YRXPf+J7nZiozvn2W/VZn3aaCr9as0YT+x0Wu/147N7/s3fe4VFVWx9+z5T03gMhJBAglBC6dMQGFkRRURHsvSvovXr9VK7YO3YFG2C7KAKKYgGxIRB6CaElhJDee6bt74+VhAQSSEIqnPd55knmzCl7ZpKZ3177t9b69Vf5ALNaJWt++HD5Qzn7bHmzWzvBqBEopW6t+l3TtLXAnjYcjk49FBXJd4dekvPUIKc0h8dXP87ZkWczuddxqpTo6FShlOowt6ioKHXLLbeoZcuWqSo2blTKYFDqrrvUKcuYMUoNHSq///ijUkajUr/8opTDodSQIUp5eChlNisFSp19tlJeXkqdf77sX1qqVEqKUhkZ8jo9/vix57dYlPL0VCowUCmr9djHS0qU8vFR6pprWu45noq89568J/v2NfyYkhKlunRRauBApWw22fb550r5+irl6qrU3XfLT1Bq8uSWGbdOC2G3K7Vli1Ivv6zUBRfIPy4opWnyhs+apdQPPyhVXNzgUwJxqo0/lwEn4B7gQWAUENSa1x88eHCDX6/Tmfh4+XNbuLCtR6LTHDzw4wPKMNugtqVva+uh6LQjjved0KEi/N7e3rz3Xu2l27vvFkvrnDltNKhWYNw4ePZZidB8+qnUV7/9dnjmGfGHz58Pl18O6eni9X/5ZbEEPfWUNPNKSpKOww4HXFpHXo/ZDM89J5Hiuqo3uLlJB+LPPhPXgZtbiz/lWuzfL3kMo0fLisOBAzKGhvjcW5Jt2+R9yciQevc1Kz+BWHf8/I5dUTkebm5SFWnaNGmQ5ecnLo/hw6VRW8+esvrzyiuS56HTzjlwAH75RSL4q1eL3w7kH3L6dIngjx8vjT86Lq8iJZx9gW+BJ4AOnkl16qE33Tp1yCjO4J24d5jRfwYxwXqfO50GUt9MoD3e+vcfrGJilLrnHpnJbNkiEYu5c5t1gtTu+PlneZ5LlkhQcMAAue/kpFT37hKhr0lFhVK9esk+3bsr9Z//KNW5s1J9+8qqQFNYvVrO98UXJ/10GkV5uTwHUGr8eKUuv1wColFRSpWVte5YlFIqJ0epJ55QatgwGZOHh1IuLkr16aPU+vVKvfSSUp98Ivv276/UhAmNv4bDodQLLyg1caJS0dFKPf103SsvOu2UPXuUuukmpSIi5I8ElAoNVWr6dKU++kip5ORmuxTtI8L/UOXPf9X82Vo3PcLfMD7+WP4U9+xp65HonCwP/fSQMsw2qITshLYeik4743jfCW0u4htzc3EZXC1009OVmjlTrCzZ2c3+mrUriouVMpmU6tdP3rGffhLtAEp9+mndx2zcqNTs2UoVFcl9m00mAk3FZlOqUyelLr5YBOl334n4bWleeEGe5913i+XIy0up666TbXPmtPz1a+JwiAjXNKWGD1fqmWfkNVi1Sik3tyPazmCQ199oVOqxx1p3jDptjM2mVGysUu7uSl1yiVJvvKHUrl1Nn2mfgHYi+N8BnIGHkVLPb7bm9XXB3zCeflo+n0pK2nokOidDVkmWcn/aXU37elpbD0WnHXK874QOZempqJAEyDvvlM6hixZJhZmOvRp+YtzdYehQyenz9xcHwBlnSN32yy6r+5hBg+RWhdEot6ZiNErn1jfegClTJAF40CCpGtjcSaN2u1QgNBjElnTRRXLdl18WW5KLCxQXS5Wb6dPrrkjUEixaJEmzc+dKInMV48fL67B2LYwYAWedBVdeKc9j2LDWGZtOO2HBAti6FT7/XP5hTg9WAomAAm6lHTdmPJ05fFiKQLS2JVOneflkyyeUWEv4z5j/tPVQdDoYLVLbTdO0EE3T5mmatqHGNhdN097UNO0RTdM+1DStZ43Hpmua9nJlJ9/b6jtv9+5SivDcc8W/np4O117bEs+g/VFVanTKFPHZe3nB1KknJ+Iby7RpUkRk2TKp2rN1q+QEVFQ03zWUkr5CfftKffnychH6IIVKXFzk91deET//7NnNd+3jkZUF998vgv7OO499fOhQuPde+TlrFuzbd2S7TgfHZpNEit27pTFFfZSWwn/+I7O8K69svfG1EZqmhQMopb4F+gCTgSFKqeVtOjCdOklJ0ZtunQokFyTj4+JDn8A+bT0UnQ5GSxVzHg0sBWoWhr0fSFZKPYskec0H0DQtDJgFzFJKPQzcrGlaj7pO6u0tP++5R753fX2lUdHpwIQJ8vOaa9puDIMGSZLqL7/A++/DvHmSi/jBB813jQ8+kBKWDz4oJUaXLJFE1aMJD5fVnd9+a75rH4/nnpOypR98cOJJ1oMPSgJvWFjbJxbrnCTl5ZIRP3aszEBDQ2FXHWXmlZKZXmqqzFBPj5rY72uaNlLTNE0pla+UilNK5bf2IKoaby1frs8zjsfhw3rC7qlAbnkufq5+bT0MnQ5Ii1h6lFKLNU0786jNFwKPVj6+XdO0WE3TvIAJwMZK7xHAWuB8YG9957/gAukOO3GiNKk6HRg3DpKToUuXthuDpsG//33k/vXXS/3+pUulWlJT+ewzqThz5pmwcKEULnnxxRP3Fho2DBYvlu7KAQFNv/6JKCgQoX/llbLycCI8PaVxVklJy41JpxUoLRXf3KpVsqQYFibd8G6+WSL+VTM/pWT7O+9IU4zRo9t23K3HKiAAeFqTrj/bgB+VUnmtOQi98VbDSEmRpn06HZvcMl3w6zSN1vTwBwFFNe4XVm6rb/sxZGVlMWTIEACcnDSiom5BLKOnB20p9utj0iSx1xQUHFmBgYYvHyslJVWzs8X+bDZLKdGGNBKtssts2HCkM3Bd7NolPn939xOfsy7mz5eSqA80wpk8ZkzTrqXTjnjkERH7n34KM2Yc2X7ttbL8NGqUPL54MWzeDPfdJzPV0wSl1AuVvy4D0DTtPGCNpmkFSin9P6AdYbFAZqZu6TkV0AW/TlNpzf7smUDN9E6vym31bT+GwMBA4uLiiIuLY+PGDdx22+kj9tsrkyaJxXnlyiPbPv9cJifz5p34+N9/h/h40UnZ2VK2PDy8YdcePFhWHTZsqH+f+HiIiZHVg7zKuGNRUf3716SkRIK8r78ujo7KuabOqYrdLstVmZkSwX/jDVm6qin2p0+H884T+86IEeLZd3aWagKvvnq6WHmq0TRtiKZpT2qathH4CFgPvNLGw9I5irQ0Ca7olp6OT25ZLr4uvm09DJ0OSGsK/u+BEQCapsUAW5VShUiFh8GVS8JU7vNDK45L5yQYMUIqB1XZZ4uLRQsBPPywaKfj8e674OMjdhlXV2k01VA8PcVWvX59/fs8+6wk+27bJqL/zDMl4fnVV+s/xm6XsXt6yqpAcrI4NXROYfLyxCt4ySUy45wyBSIi5A+oJpoGH38sHrRvvhHP/tq1Uk3g9BP7acDngB24VSnVWSl1s1JqSRsPTecoqppu6RH+jo8e4ddpKi1VpWccMAMI1TTtMU3TXIHXga6apj0GzARuAlBKpQAvAa9qmvYyME8pVa9/X6d9YTRK4vSKFRLpf/ZZ0UBVNpiHH67/2MxM8bpff33TS8UNGyYR/uoMkBocOCD5AXfcIYHbPXsk0jV0KPzrX7Bly7HHJCRIqdMXX5Rg7tNPi3vjoouaNj6dds6GDfDoozBwoHTCfekluPFGidp/9BF4eBx7TGgo/N//SYmq0NDWH3MlVoejza5dyVlIDX4f4HxN0/SaVO0UvcvuqYFDOXTBr9NkWippdw2wpo6H6my3rpRaCCxsibHotDyTJonNuXdvSEoSB8SNN8LevVLd5uh+AenpIqi/+ELKfN5WbyHWEzN0qARcDx061gr0/PMyIZk1Czp1EsuQmxvk5kL//lJmdMMGieJv3SoCf8cOOeboWvs6bUxCAuzcKSK7rkh6UpK8cY1JdFmxQmarRqP48T/7DEaOlMfefrtZht0SJJSWMj8tjU/S09t0HEqpeCAeoLIAw0Wapr0OJCilbmjTwenUIiVFfuqCv2NTVFGEQzl0wa/TJFrT0qNzinLBBWLJ6d9fatS/UungffxxGD5cSon++adsi4sTL/wbb4hYX7ECoqObfu2qxN2jbT0HD8pE4MYbReyDCHtNEwvSJ59IWfXRo6WvwNlni6tj7lzRjrrYb0ekpIgX67LLxD+flFT78b//lj++/v3l94aQnCwz0wEDpMnCmjVHxH47pNRu55P0dMZu3kz0+vW8mpLCyJpZ8m2ApmnTNE0L0jTtBuBj4C3ACtRRt1SnLTl8WHqYNMYyqdP+yC3LBdAFv06T6FCddnXaJ25uEq0/GldX8faPGiU6rXNnicQHB0tkPTb25K/dv7949N9+WxJsL7xQBP2TT4q4/089zQjPOUfGNn06TJ4s3tbffpPmbjqIP+vBB8UrNXt22ymF0lLx1RcXyzhefFGWkmbOlPr4W7bI7KzKWnPOOVIrdtIk+Ocf8WPl5kpjhEcekT8Qi0W61lmt8L//SUOPdohSik3FxcxLS+OzjAwK7XZ6uLryfLduXBscTIizM22cNfAmYEPysL4CbmyLOvw6JyYlRT5/T7M0k1OOvHKpPKELfp0moZTqMLfBgwcrnY5HUpJSt96q1NVXK3XHHUplZDTv+W+9VSkXF6VAqYgIpZYsUcpgUOrBB0987P79St15p1L79jXvmDo0VqtSV10lL6jBoFRgoFJPPaXUn38qZbO17ljuuUcpTVNq2TK5n5ys1LRpMraqW58+SqWmyh/WsGG1H+vVS6mpU5Xq3l0pd3elEhKUuu8+eWzx4tZ9Lg0k12JRb6akqAEbNihWr1Yua9aoGbt2qTV5ecrhcNTaF4hTbfR5DLwMGNrq+lW3qKgodcstt6hlVX8jOscwerRS48Y1bF+Hw6EWbVukyqxlLTomncbz8/6fFU+i/jj4R1sPRaedcrzvBE3Vle3YTunRo4caP348kyZNYtKkSW09HJ12hM0mbo4rrpBkYE9PSdptyYZcpxxvvCGdz6rqoz73nLR4vvdeKVMJ4qH/+uvWCRXu2yfR/JtuknJONdm8WXz90dHQp48s84DI/J07pQV0165w8cXS1KGq65Crq/x+330S/W8nKKVYk5/P/PR0FmdlUe5wMMjDg5tDQ7k6KAgfs7nO4zRN26iUOq0Lxg4ZMkTFxcW19TDaLUpJftPYsbBo0Yn3356xnf7v9uejyR9x/YDrsdqtLNi2gGtirsHZ5Ex+eT5f7PiC2wbfhqYvGbQqX+38iisXX8nOO3fSJ7BPWw9Hpx1yvO+EDmXp0Tsq6tSHySRfaGvXwtVXS28kXew3gmXLRNgPHCheq0cfFaEN0iwhO1sSHJ56SvxTd9WZf9+8PPaYCPknnjj2sYED5XY0miZtuPv1q709LEwSNy66CM44Q2w/7YC0igo+SU9nfno6+8rK8DYauTEkhJtCQxnk6XniE+jonIBNm2SO29BmgCmFkuG7O3s3ACv3r+SmZTeRXpzOo2Me5dFfH+WduHcYETaC2JBm8GXqNJgqD79eh1+nKXQowa+jcyK6dYN169p6FO0ch0Oi4KmpUFYm7Y2vuw4GDYK//pLsvqMJCBAP/caN4p/ftElKGw0eDA89BFFRtfcvLpYVgvx8mY2ZTNKK+e676/bMHzokWdZnny1ifdky+PJLKX/ZXKUvL7xQVipqrgi0ATaHgx9zc5mXlsZ3OTnYgXHe3jzRtStTAgNxMxrbbGw6px6ffip/7lde2bD904ul+tOenD0A7MzcCcDzfz3PWZFn8cGmD6r3i0UX/K1JteB31QW/TuPRBb+OzqnCV1+JLWfYMCk/lJkJu3aJiC8vh59/hpAQSVj9+uvax3p7SwJrXWK/Ck2T2vSDB8vxsbESNZ83D+6/H555RurXl5SIuP7zTxH3NpskyJaVSRvmFSsk0zs7Wyw7mia1WX/4QUo7VRERcaSLW3MxenTznq+SCoeDBenpHLZY+L+uXTHUYXXYX1bGh2lpfJyeTqrFQrDZzKwuXbgxNJSeTW1EoaNzHCwWqTZ78cUNz01PK04Djgj++Ox4PJw8KLYUc86n5+BQ0v8hoySjRcasc4RSaylu5iOfDbllubiZ3XAxHedzWkenHnTBr6PTUcnOls5gPXpIc4OnnhLf+vr1R2qjurqKhSU+XspaTp4sYv1f/xIV4OIiVWx69WpYDfugINi/XyL2BoN0Mps9W663erWUz/zjD1kB+Oyz2mHFNWuk4k50NFRUyLYXX4SYGBH7Tz4pz2XPHvFnjRolE4g2psLh4L3UVHaUlJBaUYFB0wgwm7kvLIw+bm7MT0tjzsGDHLZYAPAwGplZ+VqW2+0syc5mXloaq/LzMQDn+/nxVmgoF/r7YzbolZFPRUpLZdHskUdk4ayt+PFH+Zi47rqGH5NWJIJ/X+4+7A478dnxnNH5DCJ8Ipi/eT73DruXuevnklGsC/6W5IsdX3DD0htYf/N6YoJjAL3Lrs7JoQt+HZ2Oyr33SsS8iunTJdpusUhkv3NnscMYjRK9nzpVkhwuuURaIjc14a6mHSY0VBJqzz9fWhp/8IGU8Fy06FgPwbhxkln92mtS/3TdOrEDBQXJ/X//u10I/KN5ITmZx5OSCDSbCXN2RgFr8vP5OD2dUCcnUi0WRnl58WF0NO+lpvLvAwfo5OTEP4WFLMjIIM9mI9LFhaciIrg+JISw462inCZomhYCzAFilVJ1dujVNO0cpAv7PKXUm605vpNl8WK52e3wzTetf/1Fi2RBb/16qUg7YULDj00vEUtPhb2C5IJk4rPiuS72Oh4f9zjh3uE8MPwB3t/0vh7hb2FeX/c65bZy7l95P7/M+AVN03TBr3NS6IJfR6cj8sMPIvafeAKuv14i7cOHi4h3dpaofk2uuEJE/tKlYstp7uoakyfL7UT07g3vvSe/V1TAxInSAOGdd9ql2C+y2XgtJYWL/P1ZHhNTvT3PauXFQ4fYUFTEOz17MsnfH03TGOrpyYC4OKbFx+OkaUwJDOTm0FDG+/jUafM5jRkNLAUG1PVgZedeH2BLaw6quZg/X34uWyZNr1q7w+3DD0NRkcy9H3lE0nQaSlpRGi4mF8pt5axOWk2RpYjegb0JdA/k8XFiuQt2DyazJLOFRn9qoZRi/ub5XNjjQkI9689HUkqxKW0TA0MHsiNzB/+k/MOg0EGsSlzF0oSlXBJ9iS74dU4KfT1ZR6ejUVgo0fTeveXbPCICRow4sYj/978lwu/j0yrDPCHOztL9bNUqKffZDnknNZVcm43/69q11nZfs5lnunXj59hYLg4IqC5P6Gs280P//rzbsyepI0fyeZ8+nO3rq4v9o1BKLQaKjvN4YeU+HY69e6Ww1S23SIT/gw9a9/rp6ZKP/9RT0pT6gQcaeXxxOiPCRgCwNGEpAL0DetfaJ9gjWI/wN5Bv4r/hluW38P7G41cY/Cb+G4Z8MIQHfnyA9+Lew9nozIppK+gb2JeZP83E5rCRV56nC36dJtOhBH9BQQG33nory5cvb+uh6Oi0DRaLePFTUsS+0w6j4o3CwwPGj2+XLUBzrVZePnSI83x9Gebl1eDj+ri7c1unTvg3Jqyq02SysrIYMmRI9a2tSzd/+KG46GbPFivNBx9I3nprsXmz/GxK7oBSirTiNAaEDMDTyZOf9/8McEzN9yD3IN3D3wCsdiuP/PoIADuydtR67GD+Qa5dci2HCg4BsGzPMgDmrp/Luxvf5fI+lxPsEcyjYx7lQN4BtqRvkQi/iy74dZpGh7L06HX4dU5rSkvh1lvhl1/EljNyZFuP6JSkwuHgjZQUnk5OptBm48mIiLYeks5xCAwMpL003ioslOqy558v6S133CEpM3fdBS+/LPPbo1Gq7vmu3S4Th8ayaZP8jG1CxcwiSxGl1lJCPULp6d+TjWkb8XXxJcg9qNZ+we7BxKW2j9e8PfPBpg/Ym7uXEI8QdmQeEfw5pTlMWDiBhJwEgtyDeOHcF/hx349c1e8qzAYzC7Yt4PYhtwMwtutYAP4+9De5Zbl6SU6dJtOhIvw6OqclyclSVScsTLLx5swR375Os/NbXh4D4uJ46MABRnh5sXnIEEZ4e7f1sE4LNE1z1zQtsK3H0Vg2boTERFl8mzIFsrLg9gdyiXgtAnot5/77Jcrfq5ek2QweDJdfLpOAAQPA3R3uvFMW7UBaV1x2mRTYuuQSybffu1cmAA1h0yYpdtWIRalqqmrwh3qK4AfoHdj7mI66we7BZJVkVZfo1KnNprRN3PfDfTzy6yOM7TqWmwbexN6cvZTbyrHarUz+YjJJ+Un0D+7Pwm0LWX94PZklmVzY40I+mvwRe+7ew+hwKSEc5hVGF68u/Jr4K+W2ct3So9NkOlSEX0fntMJmg5tvhgUL5P6ll8J99zW8ZaZOnaRWVPBuairdXFy4LDAQT5OJTIuFWfv3syAjg0gXF1bExHC+v39bD/WURdO0ccAMIFTTtMeAl4HrgRjg9sp97gX6A/6apmUppb5so+HWS0KCiHi7HSIj4cABifDn+H/HwYKDvL7uVVa9OokrrpAKujabRO23b5eSmUOHyiRh3jwpdtWvHxQUwOGKeHrfsZC/v5jD0qUitp2dpaJt9+7g7y896upqprVp07E5+w2lqiRniEfIEcF/lH8fxMNvV3Zyy3IJcNNbmtfk8+2fM2PJDMxGM+dHnc+L575IXGocdmUnITuBrNIs/jr0F/Mvno+viy9TvprCzJ9moqExofsEjAYjPfx71DrnyC4jWZYglh9d8Os0FV3w6+i0V/7+Wxpb3XyzdJwND2/rEbUbHErhUApTA+rYF9psLMjI4HBFBblWK59mZFDmkMjkHXv34m4wUFgZPv1PeDj/6doVV73bbYuilFoDrDlq81tH7TMXmNtqg2oCDz8skfh775XG0C+9JDXvL/9KxNnqpNUcyDvAyJHd+P77+s/z9NMyUfjnH+lJN+aOj1iU9CJ7dtxCXmIEu3ZJpd2dO6WlRkaGrBr4+NQuuZmbK4m6d9zRtOdTHeH3CD2+4HcPBiCjOEMX/JVkl2bz+fbPuX/l/YwJH8O3V32Lj4sUSCi3lQOwI3MH2zO3YzKYmNp3Kk5GJ/xd/fn70N8M7TSUQPe6F7hGdhnJlztlvqsLfp2mogt+HZ32ysqVEg586SXphKtDjtXKW4cP815qKoV2OxN8fTnTx4debm50cXbG32ymwGZjb1kZe8vK2FVSwueZmRTa7ThpGmZN45KAAOZERpJusbA4K4sKhwMPo5HrQ0Lo7e7e1k9Rp4Pw229SdvOZZ6RY1pw5sr3cVs6P+37kwh4XsmLvCj7e8jH/Hf9fALJKsnh93ev8mfwnyQXJ/Hv0v7ll0C107arxxBNHzn3hZzsB2F+wm4nDIhg2rPa1y8okij9jhiTp2u1SgvNkEnbhSJfdUM9QzEYzJoOJkV2OzRWq8vRnlGTQl75Nu9hJYHPYmLlyJncNu6t6YtKWTP9mOou2LwLgnG7nsPSqpbU65Pbw74HZYGZH5g5+TfyV4WHD8XCShI5pMdN4Y/0bXNDjgnrPX/M90AW/TlPRBb+OTntl5UrxC5zCYj/DYuHH3FymBgYeE1VXStXyDpfb7Zy9ZQtbS0qY6OdHV2dnlufk8HV2dr3n9zAaucDPj4e6dGHIUabmbq6ujDyFX1udluWRR6Q59f33196+OnE1JdYS7hp6F3Zl5+MtH3P3sLtZsXcFs36aRX55PoNCBxHsEcxt393GsoRlLJ66GBfTkYZsOzNF8O/O3s3EqInHXNvVVVYUhgyRMSgl/evOPFMeHziwac8pvTgdJ6MTvi6++Ln6kfevvGphWpNgjyMR/rZgW8Y25q6fW13Fpi1JLkhm0fZFTIuZxh1D7mBE2AiMhtqfZU5GJ3oF9OL35N/ZmLqRJ8Ydmd3dNvg2luxewtS+U+u9RmxwLK4mV8psZbrg12kyuuDX0WmPZGWJGXf27LYeSYvxc24uM+LjybBaeSIxkaciIxni6UmGxcKcgwfZU1bG8pgYYitLmzx04ABbS0pY2q8fFweIjeAdpUizWKFMaQYAACAASURBVNhTWkqqxUKO1YqH0UgPV1d6uLkRZDYfk3Coo3OylJdLo+j/+z8R3zVZlrAMd7M74yPHU2wpZuriqQS/JAJ5eNhw5k2aR9+gvjiUg1fXvsqsn2fxxY4vuH7A9QAUW4o5WHAQgITshHrH0Lu3JPSuWiX5A3PnwldfQdeu4vFvCmnFaYR4hFT/z9Ql9uGIpaetmm9tTpOljMS8xDa5fk2+2/MdAI+PfZxeAb3q3a9fUD++2PEFIKsAVfQN6suhBw4d9xpmo5lhnYex5uAaXfDrNJkOJfir6vBPmjSJSZMmtfVwdHRajl9+kbDdeee19UhahFcPHeLB/fvp4+bGS92788KhQ1y7e3f148FmMwZN48wtW3i1e3fiS0t58/BhHggLqxb7AJqm0cnZmU4dvR+BTrvnhRekY+4118C+ffLvGR1dex+HcrBszzImRk3ExeTCJdGX8H9j/w9vZ28GhAzgzIgzq6O/Bs3AgyMeZN7meby38b1qwb8raxcAGhq7c3ZzPC64QG4g47rrrmPH1BjSikTwnwhfV19MBlObNd/aki4NmA/kHwDA7rDz2j+vccvgW/BybkJ5opPguz3fEeUXdUJrUb/AfoBMooZ1HnbcfetidPho/kj+Qxf8Ok2mQwl+vQ6/zinLwYMi8tetg9Gj5Xc/P1mz74CU2e28kpLCmvx8dpaU0M/dnbE+PvRydWVDUREvHDrE5YGBfBIdjZvRyFVBQWwoKuJAeTl2pbgiMJBMq5Vzt27lhoQENOACPz+e69atrZ+aTjujtQJB77wjtfWvuQb27JFtPY/SeJ9u/ZTUolSu6ncVIJHZKv9+XWiaxq2DbuXBnx5kW8Y2+gf3rxb8o8JHsTv7+IK/Jj4+UrX3ZEgvTqeb74n/xwyaoU2bb21Orx3h/+vQX8z6eRYGzcADIxrZWvgkKLGUsCpxFXcOvfOEK4n9gkTwj+s6DrOx8U35Hhr5EGdHnl3vqouOzonoUIJfR+eUIC0NbrtNCm4PHy6FvFetksfc3KT8BsDUqU3rvNPGpFVUMHnHDjYUFTHAw4PxPj5sLSnhscQjy+83h4bybs+eGCu/JE0GAyO8vWvVvO9qNBI3eDDbS0qIcXfHy6R/XOkcS2sFgvLzIScHHI4jgr9HjeqJJZYSHv31Uc7ofAaX9b6swee9NvZaHvn1Ed7f+D5vXvAmOzN34mx05oKoC3h01aPkl+dXV3tpadKK0+pM0q2LYPfgNonwO5SDrRlbAfHP2xy26knSin0rWlXw/3LgFyrsFVzU86IT7jsgZEB16c2m4O3izfjI8U06VkcHdMHfeigFu3dDQAAE1ii9lZIiNd2sVujW7citXz+IiKi7BWNz8t13Uhz6kkvqfjwrS0JH5sZHJE5ZKiqkKHZNlIIVK8Rzn5Iir+fUqVIz/+BBePxxyMuT7rhvvy3qoW9feOUVybp76ikpyB0dLcbcV1+FW25pkeErpXBAtdhuCvlWK8tzcqhwOAgwmxnq5UUnJyeWZGdz79695NtsfNuvH5Nr2G8KbDYSy8oodTgY4eXVIG+9l8nEKD2xVqeNcTikPr5S8u+8Z49E+2vmgb/w1wukFafx9dSvG5U34u/mz+V9LmfBtgU8d85z7MzaSXRANH2DpPpNQnYCZ4Q1sbB+A6mwVZBfnk92aTahHqENOibIPaiW4F+TtIa3NrzFwikLcTI6tdRQOZB3gGJLMSPCRrA2ZS2HCg5VJzn/fvB3ii3FrRYFX75nOV7OXowJP3FvlK4+XVl/y3pig5vQAllHpxnQBX9LUF4uFVa+/VZCQl5esH69tEs0meDii2HiRBH/d90FxcUi8n/7TX6vwtMTgoOlw+q8edJxpTlJShJRWl4OX38t0eZbb5VvtKgoMapu3y77fHlUz5vcXMlWOzpj7VTGZhMR/tln0gRr7Fipg7d1qyiAggJ5H0eOlPr577wj719enrzvYWEyKYiMhLVroX9/mTyYzVCznvyVV9bdUacZUEpxY0ICP+fmsqRfP4ZWKha7UvyYm0t8SQkAvmYzPVxdSa2o4Je8PLKtVpwMBqxKkW+z8XdBARalap27i7MzhyoqiHF357uYGAZ4etZ63NtkOmabjk5HoLBQxD7IR2JCAoTF7qWwIhgvZy/25uzlxb9f5Kp+VzGiy4hGn/+eYfewaPsi5q6by86snYwOH010gJjxd2fvblHBX1hRSMRrEeSV5wHQ2atzg44L9ggmPju++v6XO7/kf7v+x4U9LuS6Adc16xiVUty94m6u6HsFWSVZAEzpPYW1KWtJzE9kV/au6io2qxJXcXGvi5v1+lUUVhSSVpRGqGcoz/7xLB9u/pAZsTMabNEZ0qljWjR1Tg10wX+yFBSI79puF+/16tUi4lNSwNdXSibk5YmAfuAB2L9fOqd+840cHxEBP/0kEX2lZIKwd698q2zfLve/+046qaxc2bwR//vuE6E5aBBMmyblH4uKpLZbfLyEsC6/XEo/XHaZCH+A+fPh9tvl98GDpWPMyWSKtSQ//SSvnb+/iPRLLpHyFieiqEgmQZomNht3d7HafPstTJok5/3yS3mPBw8WY+8ZZ8DVV4uALymBH36AxYvldX38ccn4S0+XFROXyhJ8zZxsenQpy6N56dAhPk5Px8toZNyWLdwfFkaezcaKnBySKyrqPMbXZCLc2ZkKpXDSNNyNRu7o1IlpwcGEOjmRbrGwKj+f3/PzebhLF27v1KlBDbF0dDoK+flHft++HRL2KEruGMWI+YH8eu2vXPftdbiYXHj5vJebdP4zws5gUs9JPP/X8xRWFNI3sC+RPpGYDeZqH7/Vbm2S9/tErD+8nrzyPB4Y/gB9AvtU5x+ciGD3YDKKM6o/c3Zk7gDg+b+eZ0bsDAxa830GbMvYxttxb7Ni3woujb4Uk8HEpJ6TeOjnhziQd4CdmTuZ0nsKSxOWsmLvihYT/Fctvoof9v1Qff+mgTfx2sTXWuRaOjrNjS74m8L//icR3IQEEfBVoR+jUYR/v37w/vtwzjl1W2FeeEGi6wkJIhL9KrPuNU2i/gEBMKJGlOjNN+Gee0SAXn75yY3dbpcxr1wpXWNeeAGuv14mKyDJov36HdnfZpOI/513yu9//imR63PPlYTSN96QNpELFpzcuBpLQYFYoTIyRIBPnVrbKqWU2GSefFImI5oGjz4qt+ho+M9/RKTXJY5zcmQFJi7u2MfmzpX3orxcxHvXrnWfw91d3quj36+QE1fAaAwFNhv7y8qIKyrik/R04oqKmBoUxKUBAawtLGRbcTEeRiNuRiMVDgeLs7K4MjCQ13v04LIdO3g2ORk/k4khnp68EhXFub6+aEC21cqesjJ8Kh87nv2ni4sLQ728+JfeCVjnFKWm4F+zBnLKM8CYxa6sLPq81Ye88jwWTVlEJ89OTb7G02c9Tey7YvfoG9gXs9FMlF8UWzO2MuXLKWzL2EbC3QnH1Hg/Wf5J+QeAx8c93qhcgWD3YCrsFRRWFOLl7MX2zO2EeYURnx3P8oTlTI6efFLjKrOW4WR0wmgwVpe+TMpP4s31b9I7oDfd/bpj1IzEpcaRUZLBwJCBlFpLWbF3xQkDH01lZ9ZORnUZxcSoiQwKHXTcZlk6Ou0NXfA3hsJC6bLy0Udirxk0CKZPh1GjRPT9+qtYOO688/ied4PhiFe/Idx+u1h67r4b3noLsrNlQjGiEUvHVR7zWbMklwBgwAB5PmYzbNsmE5ajEyNNJnm+gwaJQAaJmL/+uhyXmwuffiqTktbyWh84ICJ/zx5ZIbn7bimI/dJLcMMNMqm5/XZZiZgxQyYo7u6y6rJ0KXz4oWz/+GNYuPCICN+/H/74Q86zb59M7AYNkmh9aalE5ntV1ll2cZFrtyEL09O5ISEBW+WEs7ebG9OCg1mclcXCjAzMmkaMuzspFRWUORyYNI3JAQHMj47G3Wjkj4EDKbbb8awjGdbTZCLydLJr6egchyrB7+Mji7h0lvr4M0fM5I31b3BZ78u4ut/VJ3WNmOAYpsVMY9H2RdX+/eiAaJbsXlK9z86snfQP7n9S1zmadYfXER0Q3ejE4AifCEAsR2FeYeSX5zP7zNm89s9rPPfXcyct+Ae+N5DxEeN556J3+G7vdwztNJRw73C+jv+agaEDMRlMdPXpyvd7vwegT2AfvF28WbJ7CbuydlW/hs2FzWHjcOFhru1/LY+NfaxZz62j0xp0KMFfmJvbenX4rVaxsqxZI3aQzEyxcJSWwmOPwRNPHCuOzz67ZcZiMsG774rILSkRu8nEiRLx37FDxPrMmZIEWhOLRZ7D0qVSCSYxUcpKvP8+xMRAbOyRicnxrCV9+8o1ysvFf14zU+2GG+C99+Q6LZRkWovycnnu2dliqxk/Xjz099wDN90EDz0k79fevTIJmD37SAQ+LEzsVrffLmN+6CFZqfjtN3jtNZgzR/YLChI7zvjWrYiglKLEbsfdaKwVndpSVMQjiYkklZdTarczLTiYWHd3bkhIYJSXF/eGhdHT1ZW+7u5omsar3buzpbiYIZ6eeBynso2maXWKfR0dndrkib2d0aPFYUmABE3uPeNeHhzxIIFugc0SUX594utM6D6BKL8oAGKCYliyewn/HvVvnvvrOf5M/rNZBb9SinUp67iw54WNPnZ0uKwKrzm4pnpMA0IGcE3MNcz5Yw42hw2ToWmfL2XWMhJyEtift58bB97IupR1PHnmk1wbey0/H/iZcV3HARDpE8mvib8CIvi7eHcBYHvm9mYX/IcLD2NXdrr6dG3W8+rotBYd6tveKzub9996q2kVY7KyJAm2yjttscBff0mU+JproLKbJ/n54tWeO1ciwj4+kkjr7CyJlHfeKZ7t1mb4cHkOIOMaN07EKshzWrhQxpecLBacgAA4fFhsJ126iHXoX/8Sge7UhAoKverpIDhsGPTpI6sAdQl+pU6cd5CSIqsjI0fK6sfrr8u2adNkFWPvXonCR0fDiy/K/ZUrjwjy2FgR7Z9/Dr//LpH6WbMkAbkujEZ5H3v1kq41UVHyvt9wgxwXHV07ibaFOVhezpU7d7KpuBirUgz08GBWly4EmM38WVDAc5W2mzE+PpQ7HDyfnIwCBnl4sCwm5phylT5mM2f6+rba+HV0TnWqIvxjxojg1wITcDG5EuYV1qxedX83f2bEzqi+P3PkTC7qeRFDOg1hwbYF/Jn8J3cOvbPZrpeUn0RWaRZndG58UnCwRzDRAdGsObim+jXoF9SvuilWQXkB/m5Na/lb1WnY5rBx2VeXoVBc1PMiInwiSJ+ZjotJvse7+Xbj18Rf8XDyINw7nGKLFL1ILkhu0nUbMqZwb926qNMx6VCCn6IiSTR9++2GH3PokPi2Fy4U4RkWJpaP3FyJFoNEzxcuFMH4+usi8MePl+3nny/7OBzHRvTbirAwWVeeN09KOYaFSYR/yRKJxo8eLc8vPFzqvZ97bssJWE0TofzQQ7BhAwwdeuSxDz8Uy9CsWfIe1PX67d0rr/Xhw0e2mc0SpV9yZCkbg0Gi8x98IH79ozvQGgwycauyHTWEs8+WlYnrroPnn5fncILJSYHNxl8FBdgrS1sqpciz2YgvLaXYbqe7qytjvb2rq98opSiw2cix2cixWsm1Wsmx2ci1WvEymfAzmbh9zx5K7HYeCAvDw2hkUUYG18QfqX5xeWAg7/bsiX/lRHd7cTFfZGZyb1iYXpte57SnNRpv1RT8AK5dEujh37NZxX5deDl7MbSzfKZWdVptTn/6usPrAJok+AHGho/li51f4OviSyfPTvi5+lVbg/LL85su+PNFXMcGx7I1YyuhHqEMDBkIgKv5iNUw0icSgN4BvWXF0tkTXxfflhH8lWPq6q1H+HU6Jh1LLQQHix+7f/8jVWLqYsUKmRgcPCjWHGdnePBBifAnJoqg9PaWT2+lRPBVJapeeaVEwgcOrH3O9lZ1JDwc/lujg+Mnn8itLbj2Wom8n3mmePnPPVcsMbfdJqsLTzwhVYlGjpTX/dAhSYwNDZVIvcUiP/ftk7XzG26Q93rlSvHr9+ghdqo33pCVmFdeab6xT54sk6N63l+lFJuLi8mrLEX5SkoK+TbbMftVVa/Jq3zs6qAgBnh48F5qKgeqJpb10NnJiT8HDqRf5SrTf7p2ZU1+PiZNI9LFhbCqValKYjw8iPHQuy3q6EDrNN7Kz5dYwODBle5H/91EBww94XHNyejw0Xy580uSC5KbzVayLmUdriZXYoJjmnT8uIhxvL/pfZYmLK1u2FVT8DeVqmj6mxe8yVmfnMVFPS+qc5IT6SuCv09gn+pt4d7h1cc3J3qEX6ej07EEf1iYJFHee6/8HDas9uMOhyRwvvOORLpnzhRLztSp4j2vjx49JJp/003HCv3THIvDgVnTjh9RCgqSevTTpsGNNx7Zft55kj+wbBk8+6yI9oICeR8DAiQvwN1dhH2/fsdG7S+QCgh2pbCfey5O06eD2UxBcDBGm+24/vRGcRyxf/uePbyflla97WJ/f+4LC8PbZEIDNMDDaCTSxQWTwUC2xcIbhw/zfHIyn2dmMtbbmzs6dSLQyQl/kwl/sxl/sxlfk4m8ygo7Qzw9CaxhszJoGuN1S46OTrshL09iFU5O8Ojj5cy2JtHLf3qrjqHKM/9n8p/NJvj/OfwPgzsNbrLXvspLX2wpJiZIJg1Vgr+qrn9TSMpPwmQwMSJsBP/c/E91gvDRdPOVwhd9A4/49Rsi+LdnbGf+5vm8fN7LDa56dDD/IEHuQbVWGHR0OhJtIvg1TfsHqAp72pVSZ2ua5gc8BxwAegCPKqWO7du9YIGI/SuugGeeER/+hAkSqX3pJRH7998Pzz3X8Brn/fpJZPo0RFVWeKlL0P+Sm8vV8fH0cXNjYe/edKmMNC/LzubxxETKHQ5CnJx4rls3hnfqJD78ZcskodZspvTyy/mhqIjJl1+OqaqGf0M8/ZXYleLT9HSeSEoi32bjlvBwNOCdv/+mp5sb6wYNwqkFV16eS07m/bQ07u3cmcsCA+nk5ESUm9txjwlwcmJ2ZCS3depEsd1Oz+PsH+jkdNzHdXR02gf5+RI7Aphy8z6eeMdR3RirtYgJisHL2YvfD/5Or4BeuJvd6R3YgJ4i9VBsKWZz2mbuHnZ3k8/R2asz3X27sz9vP/2CZJXc10WCFScb4Q/zCsNoMDIodFC9+8UGx3Jd7HVM6T2leltX7678fvD3457/y51f8vq617mwx4Wc2/3cBo9Jt/PodGTaKsL/o1LqyaO2PQP8opT6StO0ScBLwIxjjvT3l3KJo0dLSUyTSUT+nXdK5ZUrrhDLRwvU4D3VyLBYmLx9Ozal+LpfPzyNRv4vMZGD5eV4GI38LyuLKFdXNhUXExsXx5k+PqRZLPxTWEgfNzf6e3jwV0EBk3fsYOPgwWI9ufRSAKwOB5fv2MEPubm81aMHd3au7N54nPel3G5nXVERO0tK+LuggJWVHV6Henoyytub11NSUMC5vr6szMvj+eRk/q8FSmNmWyzMPniQNw8f5uqgIF6NisLQyL+nTs3cUEtHR6d5+flnaSsye/aJ960p+BOypSRnr4B6Chm0EEaDkZFdRvL+pvd5f9P79PTvScLdCU0+36dbP6XCXsFlvS87qXGN7TqW/Xn7j4nwn5Tgzz9Yb1S/Js4mZz6+5ONa28K9wymoKKCgvABvl7pLRR/IOwDAwu0LGyz4kwuSm2x90tFpD7SV4I/RNO1fgCuwQSn1PXAh8HTl438B9RvShw2TT2qbTSq0XH+9JPJGRkpSpy7262VbcTGr8/PxMZmYnZREhsWCSdMYsnEjZk0jy2qlr5sbGVYrM4KDeatnT9IqKrh77172lZXhYjDwUvfu3Nu5M2aDgfiSEs7YtIlLd+5kdWwsHiYTNoeDmxMS+CE3l85OTsw5eJDrQ0JwM8rSqc3hYH95udhgNI0txcV8npnJh2lp5FZ64APNZib6+XF5YCAX+/ujaRovde+OQym6uLhw9a5dPHXwIFMCA+nr7l7v8/0zP59Z+/fjajQy1NOTaDc3Il1cGO3tjfmo1YH0igreOHyYtw4fpthu585OnXilCWJfR0enfaOULATHx4vzs2al4bqoJfhzRGT39O/ZwqM8lpkjZtLJoxOJ+YlsSN3Q5PM4lIO56+YytNNQhocNP6kxzeg/g725e6vLYDaH4E/KT+Kcbuc06dgqu1NyQTIxLnUL9MT8RAC+if+Gty94G3en+r9DQFbCkwuSuajnRU0ak45Oe6CtBP/zSqn1mqYZgd81TSsCgoCiyscLAV9N00xKqeoMyaysLIYMGVJ9kltvvZVbR44Ub/i8eTB2bOs1f+qA2JVi6s6dJJSVARBgNrN6wAC8TSYu3bEDJ03j+5gYBnp61jouys2NH2Nj6zxnb3d3FvTuzaU7dtB93TpuDA1lcVYW+8rKeCoigrE+PozbsoV3UlOZ2aUL24uLuWH3bjYWF+OkaXiZTGRbrZg0jUsCArg2OJiBHh50dnY+xmbUuUbUfG5UFL/k5XH+tm181acPw729ybdaWZ2fz6r8fErsdsocDr7MzCTc2ZkgJydeS0nBWmlhinJ1ZU5kJGf5+KCAZ5OTefvwYaxKcWlAAE9FRtLnOBMJHR2djstvv8GuXfL7xo0nbrmRlyepXnCk0ZSHU+snzp/T7RzO6XYOc36fw+qk1VjsFpyMjS+z/NP+n0jISWDhpQtPuuLP+Mjx/BH5R/V9DycPDJqhyYLfYreQWpTaoAh/XVQl1R4vIn8g7wDRAdHszt7NsoRlXB1z/KZpWaVZlNnK9IRdnQ5Nmwh+pdT6yp92TdP+AMYDmYAnkA94AXk1xT5AYGAgcXFxx57QYKi/5rpONd9mZ5NQVsb8Xr0Y4eVFJ2dnvCsTX3cMHSpJqE348J8cEMDaQYN45MABnktOJtbdnaX9+jGpMjJ/nq8v/01KYkF6OjtLS/E1mXile3fSLBYyLRbO8vVlop8fQY3oDxDo5MSP/ftz+c6djNmyhUgXF/ZWTmTcDQZ8zWYqHA7u6NSJ57t1w8NkwupwkFJRQVxREf89eJCrqr7xAQNwfUgIj4SHn9Cnr6Oj07F54w2J2Ofnw/r1Jxb8VRH+zJJM4lLj6OXfunaeo/Fz9QMgryyPYI/gRh8/d91cQjxCuKLvFc09NDRNw8fFh7yypiXtphSmoFBN9svXFPx1UWwpJrMkk/vOuI93495l4faFJxT8eklOnVOBVhf8mqZFA6OUUvMrN/UAlgDfAyOAQ8Coyvs6zYRSimcOHqSHqyvXhYRgPErYn6xt5QwvL36NjSXVYiHUyanW+V7o3p2bdu8mxMmJCX5+PNSlCwFNaf51FIM9Pdk0eDAPHzhAlsXCdSEhjPH2ZriXV53JvGaDgUhXVyJdXZkSGMhPubnsKSsjx2plWlAQ0XpEX0fnlCc5WYqHPfywtOHY0ABnTH4+7At6ifBXH6PCXnFSia7NQZXgzy3LbbTgt9gt/LjvRx4a+VCTVgcagq+LL/kVTYvwJ+UnATS5ElGIRwhmg7neSj2JeWLnifKLYnr/6Tz/1/PEZ8UfNwG66lx6l12djkxbRPgLgQs1TeuERPIPAZ8BK4DnNU3rCXQHZrXB2OrErhQrc3NZnpNDpsXCpQEB9HN3Z1NxMSZN47LAQNyNDSvt1doopThYXs6K3Fw2FRczv1evY8R+c6FpWi3bTRWxHh7E1bBiNSe+ZjMf1NcF+DgYNY3z/f05vwXGpKOj03o0tvHWBx/Iz9tvh6QkKfR2PGw26cW41fU1BoYO5MOLPzyp6jjNQU3B31hSi1JRqBbNQfBx8Wmypacqmt5US49BM9DFu0u9Ef6qhN1In0jGR4zn7Q1vM+vnWXw/rf4Yox7h1zkVaHXBr5RKBabU8VAucMvxji11OPg8I4MwZ2dGeXtT4XDwa14egz09CW3BqihPJiUx5+BB3A0GfEwmvsnOrvX4ffv2cZaPD04GA+HOzkzw82OQhwdelbXaS+x23IzGOqPoDqX4OisLT6ORkd7e7CgpYVVeHkV2OxUOB5lWK1aleK5bN7q7Nq7+77biYm7fs4e1hYUA9HR1ZXpw45d/dXR0dNorjWm85XDAp59Kb8CuXaUx+BdfQHo6hITUfUx+PmCwUUQa53W7qc3FPpyc4E8pTAEgzCusWcdUk5MS/AUH0dBOanxdvbvWG+GvEvzdfLvh7+bPY2Mf46GfH2LlvpVMiJpQ75g8nTyrE5J1dDoiHarxVnxJCdPi4wEId3Ym32aj0G5nSkAAX1d1ym1myux23j58mIv8/flfnz44GQz8XVDAoYoKBnt6kmmx8HZqKluKi7FWivcXDh0CpCkTgAJCnZy4LDCQKwIDGeXtjVHTyLVamR4fzw+5x35ouxgMmDWNILOZdIuFg+Xl/DVw4DGVZeqixG5ndlISrxw6hK/ZzCvduzPU05MBHh4tWrdeR0dHpz3zxx9i6XnmGfgr+S9SO20HbmfDBqhaHKhqpF3V1y8/H/BIQ+FoUZHcGPxd/QHIKctp9LGtJfjTstNOvGMdJOUn0dmr80nZjcK9w/k18dc6H0vMT8TL2at60nTPsHt4N+5dHvr5Ic7rfl6deWz78/bT1afrSSc46+i0JR1K8HdzdeXbIUPYVlLCV5mZ+JnNFNvtfJudTabFckzSZ4XDQVpFBValqqPvjf2H/V9WFrk2Gw+EheFSadsZ7XNklt/Tza3W/SKbjd/y89lXVkZe5TeHu9HI+sJC5qWl8ebhwwSbzfiZzaRUVFDucPBWjx50d3VlbUEB0W5uTPDzw9dsPjKGzEym7trFo4mJxFZaiWYEBzPQ05M8q5V1hYU4gEKbje0lJSzMyCC5ooKbQ0N5rls3/GucS0dHR+d0ZcECae4dMWITExdNpNRaiuZyDRs2eFYL/htvhKws+OEHuZ+fD3i1vEhuDB0hwt/UJBusjgAAIABJREFUpN3maHAV7h1OalEqVrsVs7H299+BvAN08+1WrQWcTc48OOJB7lpxF/ty99HDv0et/YstxaxKXMXNA28+qTHp6LQ1HUrw+5pMxHh4EOPhwTWV1pT4khIWZ2WxICODmV264FCKBRkZPJWUxP7y8lrHn+3jw6tRUcR4SDm1ApuNn3JzuTggAOd6It9vHT5MtJsb430atpTnaTIxKSCgzseKbTZW5ObybXY2FQ4H43x8uDEkhKGVRaAn+PnVedwVQUFcl5PDS5UrBwbg1ZQUhnh6srVyZaEKIzDE05NFvXvXmojo6OjonM6UlUnPxolXJXHZNxfgUA4cykHk6LWsX38eIJafFStkX7sdjMb2Kfi9nL0wasYmC34PJw+8nE/QfOAk8HXxbZKlRynFnpw9nBV51kldv6t3VxzKweGiw8fkAhzIO3CMLevcbtJ869fEX48R/N/t+Y5yWzlT+049qTHp6LQ1HUrw15Wc1dvdnRFeXsxPS+Nif3+mxccTV1TEEE9PZoeEEObsjLPBQEpFBc8nJzMgLo4Jfn7VnVuzrFYGeXjwRZ8+9Kgsx7i5qIhvsrMpdzhYX1TE3KioZlnK8zCZmBoUxNSgoEYf+2aPHsR4eDDSy4vebm68lpLC8pwc7gsL40I/P9yMRlwNBnq6udU7edHR0dE5XfnuOygshNTYeykrKeO3635jxPwR+MT+ztoPzsNqhX37IKfSJXPggNTez8uj3Ql+TdPwdfVtsuDv4tWlRe0pPi4+lNnKqLBV4GxqeH5dQk4CqUWpjAkfc1LXrxLtqxJXcePAG6u3O5SDxPxELuhxQa39o/yi6OLVhV8O/MLtQ26v9dhXO78i1COUUeGjTmpMOjptTYcS/PUlZ90YEsIte/YQGxeHq8HAwt69uToo6Jgk2VtCQ3nl0CE+ycjgh9xcRnl58WREBI8lJtI/Lo7xPj54GI0szsqiKmYeaDZzbX3ZXK2Ih8nEzC5dqu8/GRnJk5GRbTgiHR0dnY7DP/+AU0Qca3OXM2f8HIZ2HsrA0IGUuv1BYSH8/Tfs3Qt4HQJTBdu2RdGjR2WE3/sQria3dpW06efq12TB39ITl6rXqaCigCBTwwNcK/etBGBC97qTZxvK6PDRDOk0hCd+e4Kr+12Nq1kKXqQXp1NuK6ebb7da+2uaxjndzmFpwlLsDjtGg9h3iyqKWLF3BbcNvg2DpgfSdDo2p8Rf8JVBQfibTFIqc8gQrgkOrrMijp/ZzJxu3UgaPpy9w4bxx8CB3Nm5M1uHDOHm0FAOlJXxXU4OD3fpQt6oUVjGjiV1xIjq5lQ6Ojo6Oh2TxERwOnc2vi6+3HPGPQCMCR/D/vJ1mFwq+P57KdFpuvxGuGIqW7fKcVWWnjDPlo2KN5aOIPgba+tZuX8lvfx7nXS9e4Nm4KVzXyKlMIXX/nmtenvNCj1Hc3bk2eSW5bIlfUv1tuV7llNhr9DtPDqnBKeE4Pc0mdh3xhmsHTSIri4uJ9zfqGlEublVf3h3cXHhjR492H3GGZSMGcNz3bvjYzZjNhgw6fYYHR0dnQ7Pzrw4ijt/x8wRM6v962PCx1BhryD2/Di+/x7++MuO6rwWLTCerdscwBHBH+7TPuw8Vfi7+je6So/NYSOtOK3VBH9jEnfLbeX8lvQb53U/r1nGMC5iHBf3uphn/3y2emK0L3cfIDX4j6Yqb6BmdZ/le5YT6hHKiC4jmmVMOjptySmjZn3M5mZpKNWeIjg6Ojo6OsenKrdr+fLl9e6jFCS5LcagzNXRfRDrB0DIsD/YtQv2F+7EbixBmcrZtF+KJOTlgcGn5aPijaUpEf704nQcquXLizYlwv9n8p+U2cpO2s5Tk8fGPEaRpYjv9nwHwJqDa/B18SXKL+qYfUM9Q+kb2JdfDvxSvS0+K55BoYN0O4/OKYH+V6yjo6Oj02Gpyu06XpfdvDyweO0m0NijVnWaQPdAogOiKfb/QzZ0Xlf9WErpHgoLITffjsM99ZQQ/K1RkhPA19UXaJzg/2n/T5gNZsZFjGu2cQzuNJhg92B+2PcDSilW7lvJed3Pq/boH82ZEWfy96G/qys47c3d26IdiXV0WhNd8OvoHEVeWR7/2/k//rvmv/yT8g+qRtnT9kyptZTEvERKraVtPRQdnXZFYiIQsJsIj+hjHhsfMZ64rDV0iy7CEL4OF1OlLTQgge3bIbMkAwz2din4CysKsdqtDT6mtQT/8SL8c9fN5V8//+uY7T/t/4lR4aPwcPJotnEYNAPn9ziflftWsjl9M2nFaUyMmljv/rHBsZRYSziYf5DUolRKraW64Nc5ZdCzUXXaNbuydrEkfgkKxc2DbibEo2UqJuWV5TF33Vx+3P8j6w+vx6HEv/vEb08Q4RPBVX2vYkLUBKx2qzTr0TQMmqH6pqGhUFjsFsK9wxkQMqBZxmWxW7A5bFTYKvjlwC+sTloNyBdZQk4Ce3P2olBY7VbSio90tvR08kTTNMwGMyEeIUT5RXFJ9CVc3Ovi6qY99aGUwuawYXVYsdgtWO1WfFx8jmlgo6PTUdh7wAq+++kTPOWYx6b3n847ce8w9o7/UZT7D0MixvN70h+U+O9h1SrYnXoIBrWfkpxVVP0f55fnE+ge2KBj2oPg/3Lnl2xO28x/x/+3umRnXlke2zK2MfvM2c0+lvOjzufjLR/z3zX/BY5fAahfUD8AdmTuwN3JHUAX/DqnDB1K8NdVh1+neVBKkVacxr7cfRg1I128u2AymCi2FONmdiPIPYgSSwmHiw6zOW0zG1I3sD9vP9ml2Tw25jEm9Wra+6GU4rek3/j70N+4md0oqChgbcpa4rPiySnLqY5Wa2g89ftT9AnsQ2ZJJhoanb064+XshYZGpE8kZ0acyfCw4UT4RFTnYljsFnZm7sSgGfBx8cGu7OSU5rBy/0p+P/g7A0IG0NO/J4+vfpyMkgyGdR7GY2MeY0LUBHr59+L7vd/z+Y7PefHvF3nur+ca/LzGR4znmphrqicomSWZmI1mOnt2pndg7wZNXFbsXcFVi6+iyFJUvc3L2QtnozMWu4UovyhGh4/GZDBh0Ax08+1GqEcoGSUZZJZkVj//9OJ0NqVtYmnCUgCiA6IZEDKASJ9IAtwCcCgH+3L3sSpxFUn5SVgdx0YMTQYTUX5RhHmFyevosFNYUUhBRQHFlmI6e3amX1A/PJ08MRqM9A3sy9D/b+88o+uqzoT9vKqWZHVZsmU19y4X3HHDhBISytBLwkeAeIBMMmFgmJAJJQESFhMgZUIIixBKIAxtEgxDYgzYYIOxDbjgXiTLcpNlWc2Srba/H+fcy5VVbEm3bF29z1p3Safe5+5zdjn77DJ4GieaTlB1oopJAycRExnT5rwnU9dYx8Hag+2OoqEo3eWz3bshsonpQ9rW8M/KmcXojNGsi/ot5bKZmTlXUnasjPVZ27n3XogqdArJuUm5bY4NJb6z7SbGJmKM8Q4/2RGl1aXERcWR2i81oG5xUXFER0Rz9HjbTrt7q/ZS31TPmv1rvH0oVu5dicEwN79n4++3xzlDzyFSIvnbtr8xMWsigxIHdbjvuMxxgFPg9zRL0gK/Ei70qgJ/R+PwK6fGGENpdSlr96/lRPMJZuXMouxYGc988Qwfl37MzoqdXWoKkhCdwMj0kdQ01HD5q5fz1jVvcc6wc1rtU9dYx7qD6xiVPor0+HTv+sbmRlbvW827u9/l1c2vsvnwZu82QZiQNYGzhpxFRlwGI9JHcNGoizjWcIzffPobdlfuZsrAKbTQwr7qfdQ21NJiWnh508s89blzbyTHJpPVP4voiGh2VuzkRPOJdn/DmIwxfFD8AU0tTRRmFfLWtW8xZdCUVvtcP/F6rp94PeV15Xy2/zMSYhKIj47HGIPBeNt6tpgWBCEmMoZlxct4bNVj3Ly446nYc5NyGZE+gvS4dESE+sZ6yuvKKTtWxrjMcYwbMI5HVj5CYVYhV4+/GmMMs3JnMTt3NlERXY+2xhjW7F/Dkl1LWLN/DZ+Wfsprm1+jqaUJcN4IzC+Yz2VjLiMmMoaYyBiiI6OJjogmKiKKg7UH2VK+hQO1ByitLiUqIork2GQGxA8gPzmfPVV7ePrzp6lvqve+HfFlQPwArp1wLaMzRpOdmM2MwTPI6p9FbUMtu4/u5njTcVbvW82DHz7IoWOHuHzs5TzytUcYkqpzTSg958uDWyEFJueOarNNRLhx0o3ctfQuAGYMnsHW8q1sGbSKjIFwzY9LeXyrvTX8FfUV/Of7/0ltQy1//9bfOz3GMyRnoAenEBFS+qW0qeFvbmlmf81+AD4o+sBb4P9oz0dER0QzY/AMv7ukxqUyK3cWK0pWnLJDcFJsEnnJeXx5+EuyErKIj44nOzHb706KEgp6VYE/WDQ0N7D+4HqmZk/t9aP2lB0r48m1T/LSxpfYdmRbm+1xUXGcNeQsFhYsZHjacIanDcdg2Fu1lxbTQv+Y/hxrPEbZsTLio+MZ1H8Q4zPHM3bAWCIjIjlaf5SFzy/kopcvYtyAcbSYFjLiM4iNiuX9ove9DxFDU4dSkFJATGQMK0tWUtNQgyDMzJnJny7+E5ePvdw74UlHbTh/e8FvO/ydzS3NrD+0nrX717L+4HoqjldwvOk45w8/n2nZ04iOjOZo/VGiI6NJiE5gdu5sBiUOoup4FesPrWdmzsxOa6Az4jM4b/jpjR5xRvYZ/GDGD9hbvZfDxw4DTufAxuZGSqtL2Vi2kU/3fUpJVQkbDm3AYIiLiiMjPoMzss9gVekq3tz2JmcPOZs3rnqjVSfD7iIiTB88nemDp3vXNbc0U9tQS4REEB8d32FHtq7S2NzIuoPr+PzA5/SP6U9URBSvbH6FJ9Y80ertQWZCpvdNhIf5+fP5zqTv8OtPf81rm19jZPpIFuQv4KrxVzEqfRTL9yynobmBi0dd7K2BU5RTsbtqG6TAqIy2BX6Ab0/8Nne/dzfNppnpg6fzSekn1Me+TOn24/z8k1L6RfU7ZVO4YJMe51SilB0rY8muJURIBMYYRIS7l95Nalwqd515V6tjSqtLyU0OzpuK1LjUNgX+g7UHaTbNACzbs4x7uAeAD0s+ZNrgaad8Q9FdLhh+gVPgP400fHzmeDaVbaIqqYoRaSN0hB4lbNACfzvc/ObNvLDhBSZmTeT2mbczffB0UuNSWX9wPXuq9rRqu115vJKt5VtpbG5kVMYoMhMyvbW/7f1Ni0sjLzmPMQPGkBSbRHFlMW9tf4s5eXP81u7bw77qfSx4bgG7KnYxv2A+t027jRmDZxAdGc0nez8hNiqWK8ZeQXK/5G5/R2pcKv/41j+4Y8kd3jGXD9cdpqqqiusLr+ecYeew48gOvjj4hbcAfO2Eazln6DmcNeQsv2WikRGRTBk0pU0N/alI7pfMvPx5fnHwJToymqGpQ9s0TRmVMYqzh57d6bHGGEqqSshJyvFbIbw9IiMie3TtOyI6Mpppg6cxbfA077qrxl9FY3Mjh+sOU1xZzMqSlWwu38zw1OGMTB9JQkwCmQmZnDHoDESE26bdxl++/Asr967kpS9f8r698RATGcOcvDmMzRhLXHQc+2v2k9IvhQUFC5ifP79Nm+aDtQdJi0tr81BXdqyMtLi0br01UbqPiAwEHgQmGmOmtbM9Avg5UAMUAH80xqzq7vcdbNpKbGNWhzPlDuw/kMvGXsbOip2kxqUyMn0kBsP++l2U1gSnVryreNLO5XuWe5v9Haw9yMD+A3nysydpbG7klqm3eCsMjjcdZ/fR3W3exAaK9mr491Y7Q52OTB/Jx3s/5kTTCZpNM2v3r+WOWXcEzOXWabeSFJvEgoIFp9x3/IDxLN29lOoT1ZyRfUbAnBQl2PSJXK64spiP9nxEQUoBZ2SfQXx0fIf7Pr/+eV7Y8AJXjbuKdQfXccPfbjjl+dPi0oiOiOaZdc90ySsvOY+SqhLAacpyw6QbyE7MpqSqhLS4NLITsymvK6fqeBU3Tr6RGTmn97qzvrGezw58xo1/u5FDtYdYceMKZufObrVPVwvGnZGZkMkL//SC387X1xGRHs80aSPRkdFkJ2aTnZjd5n48mdzkXG/tZF1jHW9vf5t9NfuYlz+P5pZmXtr4Eiv3ruS59c/R0NzAoMRBHD52mN+t+R3g1NINSx1GVEQU6w+tZ2fFTgpSCvjZgp8xJHUIm8o28eLGF/mo5CNS+qVwVsFZANQ01PCdSd/hmvHXUNNQw7qD65iTN0dr+fzPHOBvQEe1HFcCScaYH4lIGrBKRMYY41YP+9DY3GZVK1paoCZ2K4Mj2rbf9+XZi5/1voHytNveUr6FDYc2WNecB74q8L+57U3vui3lW2g2zd6C9gvrX+B707/HjiM7uPK1KzlQe4Bzh/pnYqtT0W6Bv8op8H+78Nvc88E9rN63msaWRppamgJS8eLr8r3p3zutfcdljqOhuYGiyiKuGX9NwJwUJdj0qgJ/eV05NSdqSIxN9K6rPlFNv6h+xETGYIxhT9UeVpSs4OO9H1NUWcTuo7vZfmS7d/8IiaAgpYCcpBz21+yn7FgZYzLGMCFzAgbDy1++zPz8+bx46YsArD+0nk1lm6ior2DiwIkMSx2GiHjbbfeP6U9GfAbgjEhQUV/hHbVFRLx/PQWG8rpyiiuL2XhoIxvLNvLdKd/lktGX8Kcv/sRvVv8GYwzZidlU1FdwrPGYtz31U58/xYUjL6SivoLiymKuHHcll4y+hLe3v837xe9T21BLXWMddY11HK0/SrNpJjEmkb9/6++nLFwpis3ER8dzxbgrWq3zvD3wDJkqIjQ2N7J2/1qWFS9j2Z5lFFcWc6L5BKPSR3HT5Jt4ZdMrXP/X673nGJE2gvvn309JVQnL9yynX1Q/GpobuO6N63joo4e8fQvum38f9y+4P2i/ty9gjHlNRBZ0sss3gCXuvhUichwYB2w4ecfi/YeYOnWqd3nRokUsWrTIu7x/v8Gkb6Ug4cpOneKi44jDaVLiKfDf9e5dFFUW8eM5Pz69HxZEkvslIwi7ju6if0x/ahtq2XJ4C8ebjgNOe/Qn1j7B3Py5nPWc80C7+JrFfHPkN4Pil9IvheLK4lbrPDX81064lns/uJd3d7/rzS9tyac8I/WAdthVwoteVeDfU7mHgY8OZH7+fMZkjGFj2UbeL3qfqIgoJg2cxL6afd5hx5JikxiRNoKxA8Zyyxm3sHDIQvZW72X1vtVsP7Kd0upSJg+czID4AXx5+Eve3P4mURFRTMiawIuXvuhtStGVZiIp/VI6fGXsITsxm8KsQi4adVGr9Y+e96h3mLKoiCiMMdQ21JIQk8CxhmM8vOJhnv7iaYanDWdq9lR+u/q3PL7qcaIiopibN5ehqUOJi4ojPjqe9Lh0pmZP5cy8M70PI4oSjvg2s4iOjGZW7ixm5c7i7rl3t9n3rjPvYsmuJRhjGJE+wvvw7ktzSzPPrnuWpz5/ipsn38zBYwf56fKfMmXQlDZxVgkomTjNeTxUu+vacMI0s3bt2g5P9MX2cog7ypjM9tvvt0dSbBID+w+kqLKI6yZcx3WF1532scEiQiJIjUulor6CC0ZcwDs73mFr+VZv85775t/HHUvu4MxnziQ5NpnlNyxnWNqwoPmlxKZ4m3l6KK0uJT46niEpQ5g8aDIPfPgA4Ix/f6q8M1iMyRjjHWZZC/xKONGrCvyjM0Yzb8I8Vu1bxXtF75GTlMOds++kqaWJNfvXMDt3NnPz5jI3by7jM8e3af88ceDEoNVudAfPuL/gFGQ8bzISYxN56OyHeOjsh7zbS6tLWVGygoVDFpKZ0G4+qCiKDxES0emkO+D0a7hpyk3cNOUmwGkeV3S0iOveuI6rx13N/IL5ZCVkkZec12EHUMUvlAGJPstJ7ro2nKCG6mpIcvu2b91quPVH+1j85xz694dVO7cCMK2g8yY9JzNp4CR2HNnBE994ohv6wcEz2+7cvLkUVxazpdwZzjgvOY9bpt7Cgx8+SHRkNO9d/15QC/vgNMs7XHeY2oZa70AMe6v3kpuUi4jw7MXPsnT3Uo4eP8o5Q4PTr+B0iIuOY3jacJ1lVwk7elWBPyEmgT9c+AcAWkxLn25Xm5OUw9Xjrw61hqKENXHRcfz16r/yw7//kFc2v8LTXzzt3Xb7zNt5+GsPn9b8AsqpEZEEIN4Ycxh4G5gHvOC24e8HbGr3wKh6/vxGObfd4LzNfPStxSybeClvrdjF1efns2HfNoiAOWO69oD2l8v+gjHGL6NkBQpPO/45eXNYu38t7+5+l4O1BynMKiQ+Op6VN64kKTaJwUmDg+5WmFUIOGPaz8yZCTht+D2jBE3ImsCErAlB9zodxmeO50j9kVbDSStKb6dXFfh14i1FUYJNTlIOr13pzFmw/ch2KuorePnLl3l81eO8s/Md5uXNIz46no9LP+bwscPMy5/nbWbn6S+ksxS3RkTmA98GBonIT4BHgRuACcAtwCvAZBG5D8gDrm+vw66Hp5cu57YbLgNgY/nnENfMyu2bufr8fHZW7IK0KEZmdq0jvC1NTDojPS6dxJhEJmROYHTGaJ5b/xyHag9x8aiLARgzYEzI3DwF/g2HNnxV4K/ee8qx8G3ggbMe8PY3UJRwoVcV+HXiLUVRQkVURBRjB4wFnBrVrw39Go9+8iivb3mdmoYapg+ezqSBk3hz25s8t/4573GREklOUg4FKQXtfnKScvrcsKDGmOXA8pNW/85newvwH6dzLiGCdZXLOHLkMtLTobR+B8TBpn3FABw4vpv4hvyADnEbKm6afBPnDz+fyIhIxmQ4hftm02xFzXl+cj6JMYlsOOT0s25sbuRAzQErRzw6mXGZ47yz7ipKuNC3chlFURQ/ccnoS7hk9CUA3knjPP8XVRaxp3IPxZXFzqfK+fte0Xvsq96HwXjP094DwZCUId7/BycN7nMPBF0hLqo/dfkfsGIFXHwxVLATgOLKIpqboTqiiNyo8Jyx+bKxl3n/963N99SuhxIRoTCr0FvgP1B7AIMhNyk4E38pitIazUUURVF6iG/tcWREpHfW6vZoaG6gpKrkq4cBn8/S3UvZX7O/zQNBbnLuVw8AiYPZX7OfLeVbAv67egPJ/RKpy9zEms1lXHRRJvVxOwAoayimpARMchH5SZeG2DLwDE0d6u1PYktn08KsQl7a+BLGGO8Y/MGa6VdRlNZogV9RFCWIxETGdPpAcKLpBHur97Z6ECiqLKK4spglu5ZwoOYAmQmZIW2fbRPJ/RI5AHxU8hHbSxdAnDMU5LHoIlZ9XgMJ5YwZGJ41/L5ERUQxMn0kURFR1rwRKswq5Pdrf8/e6r3eNvFaw68oocGOVEFRFEUBIDYqttMHAt/mQ3KDtLtPXyI+Oh5piWFb7aes2OyMRhPXmE19SjGvvlcEWTBt+NAQWwaHX533K6v6Kvh23NUafkUJLX13XEtFUZReiE0FOhuorq6m39FhlMV+zOfFTnOeKSnnQEI5723cCEBhbvjX8AOcPfRsFhQsCLWGF8+stRsObaCkqoTEmESrhzlVlHBGC/yKoihKryU5OZkz887DDPyMj3duhpYIrpy2EIDqtA8AGJrWNwr8tpEUm8SQlCE888Uz/OGzPzBt8LRQKylKn6VXFfg94/AvXrw41CqKoiiKJcwbOguij/Nl0xtE1OQzY5g7ydaQ94luSSQ9TidQChWFWYXsOrqL+QXzefWKV0Otoyh9ll7Vhl/H4VcURVFO5pKpM7l3IzSlbCfp8LkUpBQ4G1KLyJBCRLSvQ6i4e87dzM+fz/dnfN+azsSK0hfR2KcoiqL0asbn5hJxbBAtCQfIjBxBZkImUS3xNEXUkZekzXlCyYycGczImRFqDUXp8/SqJj2KoiiKcjIiQnr9TADy+49ARMiILgBg7KC+MUKPoihKZ2iBX1EURen1jIyfBcDorBEAFOYVADC5QGv4FUVRrCrwi8jXROQJEblfRO4LtY+iKIrSOzg/71IoOosFw5ya/hEZTkF/aKrW8CuKoljThl9E4oEngXHGmBMi8rqInG2Mec+zz7Zt21iwYEHIHBVFURQ7+c4lw9j44fucO9dZHpLiFPiHpGoNv6IoijUFfmAWsMcYc8JdXgl8A3iv40MURVGUvoxnuOYLL7yQ//mfC73rr5lwDXWNdYzOGB1CO0VRFDuwqcCfCdT4LFe767ykpaVRW1vrXV60aBGLFi0Kjp2iKIpl6HCTHQ/XnJ2YzT3z7wmBkaIoin3YVOAvAxJ9lpPcdV4GDBjA2rVrgyqlKIqiKIqiKL0ZmzrtfgLki0isu3wm8HYIfRRFURRFURSl12NNDb8xpk5EbgV+IyKHgQ2+HXYVRVEURVEURek61hT4AYwx7wLvhtpDURRFURRFUcIFm5r0KIqiKIqiKIriZ7TAryiKoiiKoihhTFgV+Nsbmi0UqEdr1KM1tniAHS42OIA9HmCXS2/HhrC0wcGDLS7q0Rr1aI16tMYfHr2qwO+ZYOWee9ofW7mzAFm8eHGXt3XnmO56dPe7uuPh7+/q7JjueAQiLDryCHZYdNUjUGHRnksowuJkh+6eL1zC4mQXf50vnKmqquK8887rMExsSI/9nTd1tk3T49N36KpHINOg000Hu7tN0+OuO/jDwx9h4evRk/P1qgK/Z4KVQ4cOdfnYUF6oQH5XdzxCXZjpyfk0LHq+zd/n07A49bZghkVfIzk5mfz8fC688MJT73wSeq/17HwaFj3f5u/zaVicepu/w6K73xXMsAAQY0yXvzBUuMN17gGSgap2dskAyjs4vKNjOtvWnWO669Hd7+qOh7+/q7NjuuMRiLDoyCPYYdFVj0CFRXsuoQiLkx26e75wCYuTXU51vnxjzIAunDfscPOFSuxOj/2dN3W2TdPj03foqkcg06DTTQe7u03T4647+MPDH2Hh63Gq86V0lCf0qgK/oiiKoiiKoijX+4BwAAAPN0lEQVRdo1c16VEURVEURVEUpWtogV9RFEVRFEVRwhgt8PcQEZFQOyjto9fGTvS6KOGO3uN2otfFTvS6BIdeV+AXkWEWOEwTkZsATAg7QYjIcBGJCdX3+yIieaF2AO+1+QaE/NoMCdV3+2JDfAGNM+14WHFdwgFbwlLv8bbYkC9ontAWG+KMxpe2BPq6RAXy5P5ERAYAXwcuEZFdwAfGmP8TEQnWzSIiacC5wEhgk4gkGWOqg/HdJ3lkAN8EZgLNIrLLGPNYMMPiJJeLgW+KyGfAO8aYz4Lp4ONxHjAZ+EBEEowxx0LgkYlzn84TkYPAVmPMCyISYYxpCaJHyOOL66FxprWHFdclHLAlLPUe79AlpPmC5gntuoQ8zmh8adclKNelV9Twi8h04AFgPfB9nCHYpoXgwswGaowxPwOKgVgRiXYdg/lK6pvAAGPMLcBTwL0iMikEN+lC4GFgFXA3kAeMD6aDD/OBPcaYO4ES1y/S/RvMa3MZEAvcDHwI/EpEBhpjWoLlYVF8AY0zXiy7Lr0ay8JS73EfLMoXNE/wwaI4o/HFh2BeF6sL/CKS7v57CKg1xqw3xuwD6oGmYFwY92kUEYnCeSJtEpHrgYXAL4BnPLsGwSXKjRBpwBERiTLGrHcdHnT3CYZHgvtvCXDUGLPJGLMVOAKkBvr7fTziff7Oxrk2/w+4FHgUJxIFyyXSfS04AthhHP4BvAP8MUgOIY8vrofGmdYOVlyXcMCWsNR7vF2PkOcLmie06xHyOKPxpV2P4F8XY4x1H5zagB8Cf8B5zREJRPlsvxI4B+epOcldJwF0ON9ddyewGpjtLmcAJzzLAQqLicDvgEifdXcALwHJ7nIkUAZMC/B1GQf8B/BLz3cBsT7bbwSmAnEetyB6/BRYCwx3l6cBRcDEAHpMBn4CRPisewx41Wc5BdgNjA2gR8jjSzsefT7O2HJdwuFjS1jqPd6uS8jzBc0TTnmvajnKnvgSsutiXQ2/+yR4PfC/wBKcJ/O7jTFNPrsVGmPexWl79R/g304f7ThcISK3A78GhuO+njTGlOPUGmT567vb4QKc33mnz7ongQnA10UkzhjTDDwBxAdKQkRGAN8CXsRJrP5dRK4zxpzw2W20MWYtMAP4bpA8fiQi/wT8HidSjwAwxqwBXgP6B8gjBicjW4hzr3i4H+e6LHQ9KoG/BMLB9Qh5fOnAo0/HGVuuSzhgS1jqPd4WG/IFzRPadQl5nNH40pZQXxfrCvzAKOBMY8weY8zrwP8B40Xka+B9bThQRL4H3AscCILDW8AcYCjw78BVIjJVRM4FBgNrAuCAiKQCW3BqDK4QkRwA43Q8egS4CLhGRM4HhgCbA+Hhkg9MMsaUGmOeAD4B5orIZNc1HUgSkZuB+4DDQfJYgROZY4GfAT8VkVQ3TAYCOwLkkYRTe/RnnI5pmQDG6Xx0D3CniCwQka8DOcD+AHnYEF/a8+jrccaW6xIO2BKWeo+3xYZ8QfOEttgQZzS+tCWk18XGAv86oE7cYbRwXv1sA8aKSAQwF7gQpyPQdcaY/w6Sw2bgbGPMH3Hael2Ec+P+qzGmNAAOGGOO4kSSj4HPcW4Az7YXgF8BmUAu8ENjTKAK2eC8Cq0Wkdnu8t8BA2S7y9/A6Zw0BLjGGPNckDz+AbTg1CI9ALyBk5gMAb5vjCkLhIQxptz9jauAKmCRz7bHcV7XzQMKcO6RSn99t7gdz1xCFl9OwyMoccbXw6I4Y0M6Fi7YEpaaL7TFhnyhz+cJYEe+oHnCKQlpWhayYTlFZCxQb4wpcpc9PZIjgA9wnriWGGP2icgJoJ9xerWXA5cZY1YE2aEe5wkeY8yLIhLpvgLqMSd7uOsijDEtxpgmEakBngceEZGJxulggjFmNc4N4zdEZBROe8jFxphG+WrosEZgH87rr9XGmC3i9Kwf7h5aAlxhjFkWZI/NbuQeC/zDGPOwiMQYYxoC5CE47ek8w6ntBJbhDKc11BizG8AY8zcRWWz8NOyaiBTiJFQ3GmOqfcIjaPGlGx6BjDNtPNzvCWqcEWds7TKgzhhjfH5jUK9LOGBDntAND80XgpQvaJ7QrkvI8wXNE9p1sTJfCHoNv4ikiMh/AU/j9tx3bxBPG6UGYCXOE/pP3XW1QB2AMWZtTwOjBw41nnP44ybtyMM9f4uIxIlIonvDrgCWA2+JyA/cRNVvuN91KfDPOJ1Ikt2MrsX1KQE+AwYB/+oethXY625f5qdEvbseezzn8EfC3omHca9NrDjt/hpwarUOAC+LyK1uBoA/Enb3HnkY+DnOK+rpJ5074PGlhx6BiDPtegQzzohIfxG5Gmf4wUeBG1wPz28MynUJB2zIE3rooflCAPMFzRPadQl5vqB5QrsuVucLQS3wi9O55iWcyDgX2CNOR5cod/sdOB1tynHavGWJyK+BCpzOFKF2+L0/HE7T43ac3zzUXS5wtz0J/M4Y0+gvF5fpQKMx5t+AjUAT0M/97rtF5FGcNnb/DUxxM6QjOJ1P+prHXTiReZC7bzqQgNMh7CmfAkKPEJEzgU+Bg8BV7vl3+2z/VwIcX/zg4c84cyqPHxCEOOMWvv4FSMZ5bb8ZSJevxvi+nSBcl3DAhjzBDx6aLwQ2PbbB4VQeQckT3O8Keb6geUK7LtbnC8Fu0rMX50n8E+A7OB1XBgDFIvJLnHZUDxpjigFE5J+BGGNMXZg5nI7HQOCnHg+cyRgeMH6ekc69SQ2wAPjUrcEYj1Oz1N+NMNnAY+arV9y3Ac3GmNo+6pEL/NLn2lQA/2actoL+5AvgSWPMr1y3bOAS4DERScEJjweMMXvc7YG6V3uLx2Dgfo8HAYozOLV75wOPGGOq3ALZAGCMiGzBibvBSEPCgd6SHtvi0WfyBRscuuARrDwB7EiPbXA4HY9g5QnQG/IFE9jxRofgPOkk+aw7C2es3BHu8gU4r1jO9NknIpwcbPLoxOW7OJ1HZrnLI4BNwEVBDhP1aO3hO571TODSdo5Tj+B73I7THvR/cUYC+RdgO0575YB4hMPHlnRQPU7bJajpoA0ONnl04hLUdNAGB5s8OnGxOl8IWJMe9+nmLpxxRi/2rDfGfIDzisszNNYynKGsPK89PB0+wsLBJo/OXIBncWaem+W67cC5cWMC4aIep+dhWo9nPQyY5O4vnr/qERKPx4EfA1uMMfcaZzSF3+LUKPndIxywJR1Uj9N3IYjpoA0ONnl05hLMdNAGB5s8TuFidb4QyDb8McAGYCkwS5xeywAYY7aLyHh3cQLOEESl7jZ/BoYNDjZ5dOhinLZs/w7cLiKDxGkbNw6nBiNoYaIerT08iZbLYpxXyP2NW1Xg+asewfVwmYhTo4OIzMGZWGhlgDzCAVvSQfU4TZcgp4M2ONjk0aFLkNNBGxxs8ujQxcXafEH89f0iMhrnaWcpsNkYUytOZ4WJwK3uusd99n8A55XIR8Dbxg9jsNrgYJNHN11ux5mJsBl4zhizTz1C7+EeMwNnkpnXjf+GMlOPHniIyMs4IyzsAJ731/0RDtiSDqqHX1z8ng7a4GCTR3dc3GP8mg7a4GCTR3dcbM0XelTgd19PGBG5FWds1SJgPpBgjLnBZ78f4HRyedwYs8VdlwHkGmO+6IG/FQ42efTUxV3vl7Fx1cO/Hv5CPfznISL9cNpwBmQyn96GLemgevjXxV3f43TQBgebPPzhEi4ONnn01MXafMF0v8OCAPHu/3cDE93/03HGWT3XZ99BOENm/R/wPXw6WfTkY4ODTR5+cIlRD/VQj9DE3d7+sSUs1cPvLn6JbzY42ORhi4sNDjZ5+MHF2nyhW234RWQW8BjwSxEZh/PUkw9gjDkC3IczyoCHSCALZwil503rThbdwgYHmzz85OKvmQjVQz36goff4m5vx5awVI+AuPhj0qqQO9jkYYuLDQ42efjJxd58oYtPPf1xfug/44xB+zrwE5yhh7b67BeL87QzwV3OAHL89OQVcgebPGxyUQ/1UI++9bElLNXDThcbHGzysMXFBgebPGxzCdSnqwGSgNP7eJi7XAi86f7/JfBD9/9snCekSL8LW+Bgk4dNLuqhHurRtz62hKV62Olig4NNHra42OBgk4dtLoH6dHWm3TrgNWPMXp91n7h/7wHOF2da62rgM+PHXtKWOdjkYZOLeqiHevQtbAlL9bDTxQYHmzxscbHBwSYP21wCQpcK/MZ5vPENjHzA00M6Bvg5zlTCRcZp6+R3bHCwycMmF/VQD/XoW9gSluphp4sNDjZ52OJig4NNHra5BIqu1vCfzCCgQkT+ApwAlhhj9vRcq9c52ORhk4t6qId69C1sCUv1sNPFBgebPGxxscHBJg/bXPxCt8fhF5GBwMfARuAVY8yL/hTrLQ42edjkoh7qoR59C1vCUj3sdLHBwSYPW1xscLDJwzYXf9KTGv4W4I/AL03ohiGywcEmD5tc1EM91KNvYUtYqoedLjY42ORhi4sNDjZ52ObiN3o0066iKIqiKIqiKHbTrYm3FEVRFEVRFEXpHWiBX1EURVEURVHCGC3wK4qiKIqiKEoYowV+RVEURVEURQljejoOv6KEBSIyHXgEZ4KNJUCcu+kXxpjKTo77oTHmV0FQVBRFUYKE5glKuKGj9CiKi4jcD/Q3xtzpLn8deAiYboxp6uCYYmNMQdAkFUVRlKCgeYISTmgNv6J0gDHmHRG5DzhfRG4FPgRGAS8ZY5aKyJVAipspbDXGvCwiP8OJV81AjTHmkVD5K4qiKP5D8wSlN6MFfkXpnD3AUOBxN0FPA/4BLDXGvCIijxhj7gcQkfOAmcaYc93lZSKyxBizLlTyiqIoil/RPEHplWiBX1E6Jx94HlggIrOARmBAB/sWAvEi8iN3eW8n+yqKoii9D80TlF6JFvgVpQNE5FygHzAcyDbG3CgiMcAtPrs1i4jgJOzrgVnGmIfd4xcCO4OsrSiKogQAzROU3owW+BUFEJGpwDwgRkR+AsQD0cBCIBO4XET+C6gAkkXkMmPM68DbwC8BjDF3iMh0EfkFUAOkAj9q+22KoiiKzWieoIQbOkqPoiiKoiiKooQxOvGWoiiKoiiKooQxWuBXFEVRFEVRlDBGC/yKoiiKoiiKEsZogV9RFEVRFEVRwhgt8CuKoiiKoihKGKMFfkVRFEVRFEUJY7TAryiKoiiKoihhjBb4FUVRFEVRFCWM+f+CdonEzwc5LQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 921.6x345.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# agents PnL\n",
    "print(f\"tangent portfolio agent PnL: {tangent_pnl}\")\n",
    "print(f\"random agent PnL: {random_pnl}\")\n",
    "# agents Sharpe Ratio\n",
    "print(f\"tangent Sharpe Ratio: {tangent_sr}\")\n",
    "print(f\"random Sharpe Ratio: {random_sr}\")\n",
    "# visualize\n",
    "env.render()"
   ]
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "qtrader",
   "language": "python",
   "name": "qtrader"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
